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MLOps.community

By Demetrios Brinkmann

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.
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Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75

MLOps.community Jan 21, 2022

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57:09
4 Years of the MLOps Community // Demetrios Brinkmann // #220

4 Years of the MLOps Community // Demetrios Brinkmann // #220

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/


Demetrios Brinkmann is the founder of the MLOps Community. Brinkmann fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and Machine Learning. Huge thank you to Weights & Biases for sponsoring this episode. Weights & Biases - https://wandb.ai/site MLOps podcast #220 with our very own Founder of MLOps Community, Demetrios Brinkmann, Looking Back on 4 Years of the MLOps Community. // Abstract In this lively podcast episode, Mihail Eric hosts Demetrios Brinkmann, the founder of the MLOps Community, discussing its origin, structure, and challenges. Demetrios shares amusing tales of job hunting on LinkedIn and building the community despite lacking technical expertise, emphasizing the value of sharing and humor. They delve into the practicalities of hosting events, transitioning from self-funded to sponsorship-based, and tease upcoming activities with renowned speakers. Mihail and Demetrios explore job dynamics, the importance of sustained relationships, and diverse engagement methods like newsletters and volunteering. Demetrios reflects on his journey to Germany post-company closure, envisioning a global hub for AI learning, embodying the community's mission. // Bio At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building Lego houses with his daughter. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/ Weights & Biases Free Course: https://wandb.ai/telidavies/ml-news/reports/Introducing-W-B-MLOps-Courses-Free-Course-Effective-MLOps-Model-Development--VmlldzozMDk2ODA2What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2: https://youtu.be/l52sRMVPVk0 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Timestamps: [00:00] Demetrios preferred coffee and bizarre listening [01:44] The MLOps Community Brainchild [04:22] The MLOps Community today [07:15] AI Quality in Person Conference on June 25th! [08:42] Community Quality [10:00] Community Learnings and the Genesis [17:55] The 600 Mark [20:15] The Feedback form [22:52] Demetrios' Journey and Learnings [29:01] Building full tolerance [29:55] Weights & Biases Free Course Ad [34:52] Building community involvement for professional success and networking [38:52] Balance in Community Growth [43:56] Collection of volunteers [49:00] Events Challenges [53:28] The future of MLOps Community [59:40] "Caveman" lifestyle choice [1:00:45] Stronger Hallucinogen [1:02:30] Wrap up

Mar 26, 202401:04:30
The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219

The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/

Huge thank you to ⁠Databricks⁠ AI for sponsoring this episode. Databricks - http://databricks.com/

Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing.

Davis Blalock is a Research Scientist and the first employee of Mosaic ML: a GenAI startup acquired for $1.3 billion by Databricks. MLOps podcast #219 with Databricks' Engineering Manager, Bandish Shah and Research Scientist Davis Blalock, The Art and Science of Training Large Language Models. // Abstract What's hard about language models at scale? Turns out...everything. MosaicML's Davis and Bandish share war stories and lessons learned from pushing the limits of LLM training and helping dozens of customers get LLMs into production. They cover what can go wrong at every level of the stack, how to make sure you're building the right solution, and some contrarian takes on the future of efficient models. // Bio Bandish Shah Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing. Bandish has over a decade of experience building systems for machine learning and enterprise applications. Prior to MosaicML, Bandish held engineering and development roles at SambaNova Systems where he helped develop and ship the first RDU systems from the ground up, and Oracle where he worked as an ASIC engineer for SPARC-based enterprise servers. Davis Blalock Davis Blalock is a research scientist at MosaicML. He completed his PhD at MIT, advised by Professor John Guttag. His primary work is designing high-performance machine learning algorithms. He received his M.S. from MIT and his B.S. from the University of Virginia. He is a Qualcomm Innovation Fellow, NSF Graduate Research Fellow, and Barry M. Goldwater Scholar. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links

AI Quality In-person Conference: AI Quality in Person Conference: https://www.aiqualityconference.com/ Website: http://databricks.com/ Davis Summarizes Papers ⁠Newsletter signup link Davis' Newsletters: Learning to recognize spoken words from five unlabeled examples in under two seconds: https://arxiv.org/abs/1609.09196 Training on data at 5GB/s in a single thread: https://arxiv.org/abs/1808.02515 Nearest-neighbor searching through billions of images per second in one thread with no indexing: https://arxiv.org/abs/1706.10283 Multiplying matrices 10-100x faster than a matrix multiply (with some approximation error): https://arxiv.org/abs/2106.10860 Hidden Technical Debt in Machine Learning Systems: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Davis on LinkedIn: https://www.linkedin.com/in/dblalock/ Connect with Bandish on LinkedIn: https://www.linkedin.com/in/bandish-shah/

Mar 22, 202401:15:12
Security and Privacy // Day 2 Panel 1 // AI in Production Conference

Security and Privacy // Day 2 Panel 1 // AI in Production Conference

// Abstract Diego, David, Ads, and Katharine, bring to light the risks, vulnerabilities, and evolving security landscape of machine learning as we venture into the AI-driven future. They underscore the importance of education in managing AI risks and the critical role privacy engineering plays in this narrative. They explore the legal and ethical implications of AI technologies, fostering a vital conversation on the balance between utility and privacy. // Bio Diego Oppenheimer - Moderator Diego Oppenheimer is a serial entrepreneur, product developer and investor with an extensive background in all things data. Currently, he is a Partner at Factory a venture fund specialized in AI investments as well as a co-founder at Guardrails AI. Previously he was an executive vice president at DataRobot, Founder and CEO at Algorithmia (acquired by DataRobot) and shipped some of Microsoft’s most used data analysis products including Excel, PowerBI and SQL Server. Diego is active in AI/ML communities as a founding member and strategic advisor for the AI Infrastructure Alliance and MLops.Community and works with leaders to define AI industry standards and best practices. Diego holds a Bachelor's degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University. Ads Dawson A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics. Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection for example. Ads is also a networking veteran and love a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries. Katharine Jarmul Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy. David Haber David has started and grown several technology companies. He developed safety-critical AI in the healthcare space and for autonomous flight. David has educated thousands of people and Fortune 500 companies on the topic of AI. Outside of work, he loves to spend time with his family and enjoys training for the next Ironman. A big thank you to our Premium Sponsors,  @Databricks  and  @baseten  for their generous support! // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

Mar 19, 202434:37
[Exclusive] Zilliz Roundtable // Why Purpose-built Vector Databases Matter for Your Use Case

[Exclusive] Zilliz Roundtable // Why Purpose-built Vector Databases Matter for Your Use Case

Frank Liu is the Director of Operations & ML Architect at Zilliz, where he serves as a maintainer for the Towhee open-source project. Jiang Chen is the Head of AI Platform and Ecosystem at Zilliz. Yujian Tang is a developer advocate at Zilliz. He has a background as a software engineer working on AutoML at Amazon. MLOps Coffee Sessions Special episode with Zilliz, Why Purpose-built Vector Databases Matter for Your Use Case, fueled by our Premium Brand Partner, Zilliz. Engineering deep-dive into the world of purpose-built databases optimized for vector data. In this live session, we explore why non-purpose-built databases fall short in handling vector data effectively and discuss real-world use cases demonstrating the transformative potential of purpose-built solutions. Whether you're a developer, data scientist, or database enthusiast, this virtual roundtable offers valuable insights into harnessing the full potential of vector data for your projects. // Bio Jiang Chen Frank Liu is Head of AI & ML at Zilliz, with over eight years of industry experience in machine learning and hardware engineering. Before joining Zilliz, Frank co-founded Orion Innovations, an IoT startup based in Shanghai, and worked as an ML Software Engineer at Yahoo in San Francisco. He presents at major industry events like the Open Source Summit and writes tech content for leading publications such as Towards Data Science and DZone. His passion for ML extends beyond the workplace; in his free time, he trains ML models and experiments with unique architectures. Frank holds MS and BS degrees in Electrical Engineering from Stanford University. Frank Liu Jiang Chen is the Head of AI Platform and Ecosystem at Zilliz. With years of experience in data infrastructures and information retrieval, Jiang previously served as a tech lead and product manager for Search Indexing at Google. Jiang holds a Master's degree in Computer Science from the University of Michigan, Ann Arbor. Yujian Tang Yujian Tang is a Developer Advocate at Zilliz. He has a background as a software engineer working on AutoML at Amazon. Yujian studied Computer Science, Statistics, and Neuroscience with research papers published to conferences including IEEE Big Data. He enjoys drinking bubble tea, spending time with family, and being near water. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://zilliz.com/ Neural Priming for Sample-Efficient Adaptation: https://arxiv.org/abs/2306.10191LIMA: Less Is More for Alignment: https://arxiv.org/abs/2305.11206ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT: https://arxiv.org/abs/2004.12832 Milvus Vector Database by Zilliz: https://zilliz.com/what-is-milvus --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Timestamps: [00:00] Demetrios' musical intro [04:36] Vector Databases vs. LLMs [07:51] Relevance Over Speed [12:55] Pipelines [16:19] Vector Databases Integration Benefits [26:42] Database Diversity Market [27:38] Milus vs. Pinecone [30:22] Vector DB for Training & Deployment [34:32] Future proof of AI applications [45:16] Data Size and Quality [48:53] ColBERT Model [54:25] Vector Data Consistency Best Practices [57:24] Wrap up

Mar 15, 202459:01
A Decade of AI Safety and Trust // Petar Tsankov // MLOps Podcast #218

A Decade of AI Safety and Trust // Petar Tsankov // MLOps Podcast #218

Huge thank you to LatticeFlow AI for sponsoring this episode. LatticeFlow AI - https://latticeflow.ai/.Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence. MLOps podcast #218 with Petar Tsankov, Co-Founder and CEO at LatticeFlow AI, A Decade of AI Safety and Trust. // Abstract // Bio Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems. Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://latticeflow.ai/ ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran VerX, the world's first fully automated verifier for smart contracts: https://verx.ch Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch DeGuard, de-obfuscates Android binaries: http://apk-deguard.com SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Petar on LinkedIn: https://www.linkedin.com/in/petartsankov/ Timestamps: [00:00] Petar's preferred coffee [00:29] Takeaways [03:15] Shout out to LatticeFlow for sponsoring this episode! [03:22] Please like, share, leave a review, and subscribe to our MLOps channels! [03:42] Expansion [05:16] Zurich ETH [07:06] AI Safety [09:24] Optimizing one metric, no fixed data sets [12:19] Trust life-changing issues [14:59] So much interest in GenAI [16:45] Explosion of GenAI Trust and Safety [21:14] Red Teaming [25:22] Trustworthy AI in Industry [27:43] DataOps Challenges [33:42] Trusting Third-Party Models [37:00] Testing Open Source Models [41:41] Specialized ML for Leasing [43:04] Regulation and Financial Incentives [45:30] Regulations Drive Innovation Balance [47:23] Regulations vs Certification: Voluntary Prove [52:24] Workflow Transparency: Trust & Efficiency [53:20] Engineers Balance Compliance Risks [54:53] Pushing Deep Learning Limits [57:31] Wrap up

Mar 12, 202458:04
The Real E2E RAG Stack // Sam Bean, Rewind AI // #217

The Real E2E RAG Stack // Sam Bean, Rewind AI // #217

Thank you to Zilliz our wonderful sponsors of this episode create some amazing stuff with Zilliz RAG - https://zilliz.com/vector-database-use-cases/llm-retrieval-augmented-generation

Sam Bean is a seasoned AI and machine learning expert, specializing in Large Language Models (LLMs) and search tech.

With a computer science background and a drive for innovation, Sam leads the team at Rewind AI in leveraging advanced tech to tackle complex challenges. MLOps podcast #217 with Sam Bean, Software Engineer (Applied AI) at Rewind.ai, The Real E2E RAG Stack. // Abstract What does a fully operational LLM + Search stack look like when you're running your own retrieval and inference infrastructure? What does the flywheel really mean for RAG applications? How do you maintain the quality of your responses? How do you prune/dedupe documents to maintain your document quality? // Bio Sam has been training, evaluating, and deploying production-grade inference solutions for language models for the past 2 years at You.com. Previous to that he built personalization algorithms at StockX. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://github.com/sam-h-bean/ REinforced Self Training (REST) - https://arxiv.org/pdf/2308.08998.pdf REST meets REACT - https://arxiv.org/pdf/2312.10003.pdf --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sam on LinkedIn: https://www.linkedin.com/in/samuel-h-bean/ Timestamps: [00:00] Sam's preferred coffee [00:11] Takeaways [03:52] A competitive coding pinball player [07:18] Sam's MLOps journey [10:33] Search Challenges with ML [15:04] Expensive evaluation [21:04] Labeling Parties Boost Data Quality [24:10] Zeno's Paradox of Motion [25:51] Sam's job at Rewind AI [29:35] Multimodal RAG [30:59 - 32:06] Zilliz Ad [32:07] University of Prague paper leak [36:38] Signals behind the scenes [39:28] Content Over Metadata Approach [43:22] Optionality around evaluation and search [48:35] Incremental Robustness Building [51:33] Solid Foundations for Success [53:42] Production RAGs [1:00:06] Thoughts on DSPy [1:05:40] Using DSPy in Production [1:08:26] Wrap up

Mar 08, 202401:10:06
Managing Data for Effective GenAI Application // Anu Arora and Anass Bensrhir // #215

Managing Data for Effective GenAI Application // Anu Arora and Anass Bensrhir // #215

Anass Bensrhir is the Associate Partner of McKinsey & Company Casablanca. Anu Arora is the Principal Data Engineering at McKinsey & Company.


Check out mckinsey.com/quantumblack MLOps podcast #214 with QuantumBlack AI by McKinsey's Principal Data Engineer, Anu Arora and Associate Partner, Anass Bensrhir, Managing Data for Effective GenAI Application brought to you by our Premium Brand Partner QuantumBlack AI by  @McKinsey . // Abstract Generative AI is poised to bring impact across all industries and business functions across industries While many companies pilot GenAI, only a few have deployed GenAI use cases, e.g., retailers are producing videos to answer common customer questions using ChatGPT. A majority of organizations are facing challenges to industrialize and scale, with data being one of the biggest inhibitors. Organizations need to strengthen their data foundations given that among leading organizations, 72% noted managing data among the top challenges preventing them from scaling impact. Furthermore, leaders noticed that +31% of their staff's time is spent on non-value-added tasks due to poor data quality and availability issues. // Bio Anu Arora Data architect(~12 years) and have experience in Big data technologies, API development, building scalable data pipelines including DevOps and DataOps, and building GenAI solutions. Anass Bensrhir Anass Leads QuantumBlack in Africa, he specializes in the Financial sector and helps organizations deliver successful large Data transformation programs. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Anu on LinkedIn: https://uk.linkedin.com/in/anu-arora-072012 Connect with Anass on LinkedIn: https://www.linkedin.com/in/abensrhir/ Timestamps: [00:00] Anass and Anu's preferred coffee [00:35] Takeaways [04:02] Please like, share, leave a review, and subscribe to our MLOps channels! [04:09] Huge shout out to our sponsor QuantumBlack! [04:29] Anu's tech background [06:31] Anass tech background [07:28] The landscape of data [10:37] Dealing with unstructured data [15:51] Data lakes and ETL processes [22:19] Data Engineers' Heavy Workload [29:49] Data privacy and PII in the new LLMs paradigm [36:13] Balancing LLM Adoption Risk [44:06] Effective LMS Implementation Strategy [49:00] Decisions: Create or Wait [50:39] Wrap up

Mar 05, 202451:01
Becoming an AI Evangelist // Alex Volkov // #215

Becoming an AI Evangelist // Alex Volkov // #215

Alex Volkov serves as the AI Evangelist with Weights & Biases, Host of ThursdAI, Founder and CEO Targum and AI Consultant GPU POOR Def. not an owl.

MLOps podcast #215 with Alex Volkov, AI Evangelist at Weights & Biases, Becoming an AI Evangelist. // Abstract Follow Alex's journey into the world of AI, from being interested in running his first AI models to founding an AI startup, running a successful weekly AI news podcast & newsletter, and landing a job with  @WeightsBiases . // Bio Alex Volkov is an AI Evangelist at Weights & Biases, celebrated for his expertise in clarifying the complexities of AI and advocating for its beneficial uses. He is the founder and host of ThursdAI, a weekly newsletter and podcast that explores the latest in AI, its practical applications, open-source and innovation. With a solid foundation as an AI startup founder and 20 years in full-stack software engineering, Alex offers a deep well of experience and insight into AI innovation. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Evaluation Survey: https://hq.yougot.us/primary/WebInterview/3AW6LW5D/Start Website: https://thursdai.news

Alex on X (+X spaces also are also there) - https://twitter.com/altryne/

ThursdAI podcast/newsletter - https://sub.thursdai.news

Denver local AI tinkerers meetup - https://denver-boulder.aitinkerers.org/

Weights & Biases Growth Team hack week review - https://www.youtube.com/watchInterview w/

Crew AI creator Joao Moura - https://sub.thursdai.news/p/jan14-sunday-special-deep-dives --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/alex-volkov-/

Mar 01, 202401:14:32
LLM Use Cases in Production // AI in Production Conference // Panel 1

LLM Use Cases in Production // AI in Production Conference // Panel 1

// Abstract

From startups achieving significant value with minor capabilities to AI revolutionizing sales calls and raising sales by 30%, we explore a series of interesting real-world use cases. Understanding the objectives and complexities of various industries, exploring the challenges of launching products, and highlighting the vital integration of the human touch with technology, this episode is a treasure trove of insights. // Bio Greg Kamradt - Moderator Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications. By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers. Some of his popular works: 'Introduction to LangChain Part 1, Part 2' (+145K views), and 'How To Question A Book' featuring Pinecone (+115K Views). Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools. He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company. Agnieszka Mikołajczyk-Bareła Senior AI Engineer@Chaptr working on LLMs. PhD, author of datasets, scientific papers, and publications with over 1800 citations, holding numerous scholarships and awards. Daily, she conducts her research on her grant "Detecting and overcoming bias in data with explainable artificial intelligence" Preludium, awarded by Polish National Centre. She is a co-organizer of PolEval2021 and PolEval 2022 tasks with punctuation prediction and restoration. She organizes and actively contributes to the scientific community in her free time: she managed and led the team during the HearAI project focused on modeling Sign Language. A former organizer and a team leader at the open-source project. As an ML Expert, she supports the project "Susana" designed to detect and read product expiry dates to help the Blind "see". Jason Liu Jason is a machine learning engineer and technical advisor. Arjun Kannan Arjun Kannan builds products, businesses, and teams. Currently building ResiDesk, bringing AI copilots to help real estate forecast renewals, reduce turnover, and hit their budget. Arjun built and led product and engineering functions at Climb Credit (serving 100k students, doubling loan growth for 3 years straight) and at BlackRock (creating $400mm in annual revenue), and helped build multiple startups and small companies before that. // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

Feb 28, 202430:48
Information Retrieval & Relevance // Daniel Svonava // #214

Information Retrieval & Relevance // Daniel Svonava // #214

Daniel Svonava is the Co-Founder of Superlinked. Daniel Svonava attended the Faculty of Informatics and Information Technologies, Slovak University of Technology. MLOps podcast #214 with Daniel Svonava, CEO & Co-founder at Superlinked, Information Retrieval & Relevance: Vector Embeddings for Semantic Search // Abstract In today's information-rich world, the ability to retrieve relevant information effectively is essential. This lecture explores the transformative power of vector embeddings, revolutionizing information retrieval by capturing semantic meaning and context. We'll delve into: - The fundamental concepts of vector embeddings and their role in semantic search - Techniques for creating meaningful vector representations of text and data - Algorithmic approaches for efficient vector similarity search and retrieval - Practical strategies for applying vector embeddings in information retrieval systems // Bio Daniel is an entrepreneurial technologist with a 20 year career starting with competitive programming and web development in highschool, algorithm research and Google & IBM Research internships during university, first entrepreneurial steps with his own computational photography startup and a 6 year tenure as a tech lead for ML infrastructure at YouTube Ads, where his ad performance forecasting engine powers the purchase of $10B of ads per year. Presently, Daniel is a co-founder of Superlinked.com - a ML infrastructure startup that makes it easier to build information-retrieval heavy systems - from Recommender Engines to Enterprise-focused LLM apps. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Daniel on LinkedIn: https://www.linkedin.com/in/svonava/?originalSubdomain=ch

Feb 24, 202456:05
Evaluating and Integrating ML Models // Morgan McGuire and Anish Shah // #213

Evaluating and Integrating ML Models // Morgan McGuire and Anish Shah // #213

Morgan McGuire has held a variety of roles in the past 13 years. In 2008, he completed a Research Internship at Queen Mary, University of London. Currently, he is the Head of Growth ML and Growth ML Engineer at Weights & Biases. Anish Shah has been working in the tech industry since 2015. In 2015, he was a Technical Support at Fox School of Business at Temple University. In 2021, he has been an MLOps Engineer - Growth and a Tier 2 Support Machine Learning Engineer at Weights & Biases. ______________________________________________ Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches. Join the AI in Production Conference on February 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15 ______________________________________________ MLOps podcast #213 with Weights and Biases' Growth Director, Morgan McGuire and MLE, Anish Shah, Evaluating and Integrating ML Models brought to you by our Premium Brand Partner  @WeightsBiases. // Abstract Anish Shah and Morgan McGuire share insights on their journey into ML, the exciting work they're doing at Weights and Biases, and their thoughts on MLOps. They discuss using large language models (LLMs) for translation, pre-written code, and internal support. They discuss the challenges of integrating LLMs into products, the need for real use cases, and maintaining credibility. They also touch on evaluating ML models collaboratively and the importance of continual improvement. They emphasize understanding retrieval and balancing novelty with precision. This episode provides a deep dive into Weights and Biases' work with LLMs and the future of ML evaluation in MLOps. It's a must-listen for anyone interested in LLMs and ML evaluation. // Bio Anish Shah Anish loves turning ML ideas into ML products. He started his career working with multiple Data Science teams within SAP, working with traditional ML, deep learning, and recommendation systems before landing at Weights & Biases. With the art of programming and a little magic, Anish crafts ML projects to help better serve our customers, turning “oh nos” to “a-ha”s! Morgan McGuire Morgan is a Growth Director and an ML Engineer at Weights & Biases. He has a background in NLP and previously worked at Facebook on the Safety team where he helped classify and flag potentially high-severity content for removal. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI in Production Conference: https://home.mlops.community/home/events/ai-in-production-2024-02-15 Website: https://wandb.ai/ Prompt Templates the Song: https://www.youtube.com/watch?v=g6WT85gIsE8 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Morgan on LinkedIn: https://www.linkedin.com/in/morganmcg1/ Connect with Anish on LinkedIn: https://www.linkedin.com/in/anish-shah/

Feb 21, 202451:56
Data Governance and AI // Alexandra Diem // #212

Data Governance and AI // Alexandra Diem // #212

Alexandra Diem, PhD, has extensive experience in the field of AI, machine learning, and cloud analytics. Alexandra currently holds the position of Head of Cloud Analytics and MLOps at Gjensidige. Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches. Join the AI in Production Conference on February 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15 _____________________________________________________________ MLOps podcast #212 with Alexandra Diem, Head of Cloud Analytics & MLOps at Gjensidige, Data Governance and AI. // Abstract This recent session featuring the incredibly talented Alexandra Diem delves into the challenges of generative AI in sensitive data environments, the emergence of specialized chatbots, and data governance. Balancing high-tech projects with those offering significant business value, using agile methods, is also discussed. Alexandra's journey from academia to being a consultant in Norway is truly inspiring. The discussion explores the function of enabling and R&D in tech roles, the shift towards self-serve solutions, and the integration of AI into existing workflows. Stimulating conversations about future-oriented technologies married with sound data science and industry practices make this session a must-listen for anyone interested in machine learning operations! // Bio Former academic turned data scientist with a passion for data mesh architectures. 🔬 Background in applied mathematics and statistics, adept at leveraging data-driven insights to solve complex problems. Experienced in diverse domains spanning the private and public sectors. 🧠 Made significant contributions to research in physiological modeling, successfully debunking a leading biomedical hypothesis on Alzheimer's disease during my PhD. Developed innovative approaches to quantify blood supply to the heart. 💡 Solution-oriented thinker with a track record of efficiently tackling challenging problems and adapting to novel scenarios. ⚙️ Expertise: Data Science | Mathematical Modeling | Statistical Analysis | Problem Solving In my spare time, you'll find me exploring the great outdoors—whether it's pedaling through scenic landscapes on a bike or riding down the slopes on a pair of skis. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI in Production Conference: https://home.mlops.community/home/events/ai-in-production-2024-02-15 Website: https://github.com/alexdiem Talk "DevOps revolutionised software engineering, it's time to revolutionise data" https://vimeo.com/861721829 from JavaZone 2023 Zilliz Cloud: https://zilliz.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alexandra on LinkedIn: https://www.linkedin.com/in/dralexdiem/

Feb 16, 202401:05:45
Ads Ranking Evolution at Pinterest // Aayush Mudgal // #211

Ads Ranking Evolution at Pinterest // Aayush Mudgal // #211

Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy-Aware Conversion Modeling. Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches. Join the AI in Production Conference on February 15 and 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15 ________________________________________________________________________________________ MLOps podcast #211 with Aayush Mudgal, Senior Machine Learning Engineer at Pinterest, Ads Ranking Evolution at Pinterest. // Abstract Listen to the lessons from the journey of scaling ads ranking at Pinterest using innovative machine learning algorithms and innovation in the ML platform. Learn how they transitioned from traditional logistic regressions to deep learning-based transformer models, incorporating sequential signals, multi-task learning, and transfer learning. Discover the hurdles Pinterest overcame and the insights they gained in this talk, as Aayush shares the transformation of ads ranking at Pinterest and the lessons learned along the way. Discover how ML Platform evolution is crucial for algorithmic advancements. // Bio Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy-Aware Conversion Modeling. He has a successful track record of starting and executing 0 to 1 projects, including conversion optimization, video ads ranking, landing page optimization, and evolving the ads ranking from GBDT to DNN stack. His expertise is in large-scale recommendation systems, personalization, and ads marketplaces. Before entering the industry, Aayush conducted research on intelligent tutoring systems, developing data-driven feedback to aid students in learning computer programming. He holds a Master's in Computer Science from Columbia University and a Bachelor of Technology in Computer Science from the Indian Institute of Technology Kanpur. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.youtube.com/watch?v=MZVIxtsGzBg https://www.youtube.com/watch?v=ffpPUr8Hg6U --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aayush on LinkedIn: https://www.linkedin.com/in/aayushmudgal/

Feb 13, 202452:37
LLM Evaluation with Arize AI's Aparna Dhinakaran // #210

LLM Evaluation with Arize AI's Aparna Dhinakaran // #210

Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches. Join the AI in Production Conference on February 15 and 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15 ________________________________________________________________________________________
Aparna Dhinakaran is the Co-Founder and Chief Product Officer at 
Arize AI, a pioneer and early leader in machine learning (ML) observability. MLOps podcast #210 with Aparna Dhinakaran, Co-Founder and Chief Product Officer of Arize AI, LLM Evaluation with Arize AI's Aparna Dhinakaran. // Abstract Dive into the complexities of Language Model (LLM) evaluation, the role of the Phoenix evaluations library, and the importance of highly customized evaluations in software application. The discourse delves into the nuances of fine-tuning in AI, the debate between the use of open-source versus private models, and the urgency of getting models into production for early identification of bottlenecks. Then examine the relevance of retrieved information, output legitimacy, and the operational advantages of Phoenix in supporting LLM evaluations. // Bio Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in AI observability and LLM evaluation. A frequent speaker at top conferences and thought leader in the space, Dhinakaran is a Forbes 30 Under 30 honoree. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Arize-Phoenix: https://phoenix.arize.com/ Phoenix LLM task eval library: https://docs.arize.com/phoenix/llm-evals/running-pre-tested-evals Aparna's recent piece on LLM evaluation: https://arize.com/blog-course/llm-evaluation-the-definitive-guide/ Thread on the difference between model and task LLM evals: https://twitter.com/aparnadhinak/status/1752763354320404488 Research thread on why numeric score evals are broken: https://twitter.com/aparnadhinak/status/1748368364395721128 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aparna on LinkedIn: https://www.linkedin.com/in/aparnadhinakaran/

Feb 09, 202455:47
Powering MLOps: The Story of Tecton's Rift // Matt Bleifer & Mike Eastham // #209

Powering MLOps: The Story of Tecton's Rift // Matt Bleifer & Mike Eastham // #209

Matt Bleifer is a Group Product Manager at Tecton, where he focuses on the core product experience such as building, testing, and productionizing feature pipelines as scale. Michael Eastham works as a Chief Architect at Tecton, which is a Business Intelligence (BI) Software company with an estimated 100 employees Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches. Join the AI in Production Conference on February 15 and 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15 ________________________________________________________________________________________ MLOps podcast #209 with Tecton's Group Product Manager, Matt Bleifer and Chief Architect, Mike Eastham, Powering MLOps: The Story of Tecton's Rift brought to us by our Premium Brand Partner,  @tecton8241 . // Abstract Explore the intricacies of feature platforms and their integration in the data realm. Compare traditional predictive machine learning with the integration of Linguistic Model Systems into software applications. Get a glimpse of Rift, a product enhancing data processing with smooth compatibility with various technologies. Join in on the journey of developing Rift, and making Tecton user-friendly, and enjoy Matt's insights and contributions. Wrap it up with lighthearted talks on future collaborations, music, and a touch of nostalgia. // Bio Matt Bleifer Matt Bleifer is a Group Product Manager and an early employee at Tecton. He focuses on core product experiences such as building, testing, and productionizing feature pipelines at scale. Before joining Tecton, he was a Product Manager for Machine Learning at both Twitter and Workday, totaling nearly a decade of working on machine learning platforms. Matt has a Bachelor’s Degree in Computer Science from California Polytechnic State University, San Luis Obispo. Michael Eastham Michael Eastham is the Chief at Tecton. Previously, he was a software engineer at Google, working on Web Search. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.tecton.ai/Rift Article: https://www.tecton.ai/blog/unlocking-real-time-ai-for-everyone-with-tecton/ Rift: https://resources.tecton.ai/riftBig Data is Dead blog: https://motherduck.com/blog/big-data-is-dead/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Matt on LinkedIn: https://www.linkedin.com/in/mattbleifer/Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikeeastham/ Timestamps: [00:00] AI in Production Conference [02:13] Matt & Mike's preferred coffee [02:37] Takeaways [04:50] Matt & Mike's Tecton titles [06:49] Matt's background in tech [09:49] Mike's background in tech [12:53] Tecton refresher [18:23] Feature store to Feature platform [21:06] Current evolution of Tecton [24:41] The understatement [26:12] Duck DB Con [27:54] Rift [30:10] Kafka Flink [33:36] What is large in aggregations? [38:09] Big Data is Dead! [41:14] Principles of creating Rift [45:54] The battle between Simplicity and Flexibility [47:28] Is he serious? Segment [50:54] Can you get any more hype Segment [57:10] What are you excited about? [1:02:45] Wrap up

Feb 06, 202401:03:58
[Exclusive] QuantumBlack Round-table // Gen AI Buy vs Build, Commercial vs Open Source

[Exclusive] QuantumBlack Round-table // Gen AI Buy vs Build, Commercial vs Open Source

Join our virtual conference 'AI in Production'

Transform faster. Innovate smarter. Anticipate the future. At QuantumBlack, we unlock the power of artificial intelligence (AI) to help organizations reinvent themselves from the ground up—and accelerate sustainable and inclusive growth.

MLOps Coffee Sessions Special episode with QuantumBlack, AI by McKinsey, GenAI Buy vs Build, Commercial vs Open Source, fueled by our Premium Brand Partner, QuantumBlack, AI by McKinsey. // Abstract Do you build or buy? Check the QuantumBlack team discussing the different sides of buying vs building your own GenAI solution. Let's look at the trade-offs companies need to make - including some of the considerations of using black box solutions that do not provide transparency on what data sources were used. Whether you are a business leader or a developer exploring the space of GenAI, this talk provides you with valuable insights to prepare you for how you can be more informed and prepared for navigating this fast-moving space. // Bio Ilona Logvinova Ilona Logvinova is the Head of Innovation for McKinsey Legal, working across the legal department to identify, lead, and implement cross-cutting and impactful innovation initiatives, covering legal technologies and reimagination of the profession initiatives. At McKinsey Ilona is also Managing Counsel for McKinsey Digital, working closely with emerging technologies across use cases and industries. Mohamed Abusaid Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with a computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products. Nayur Khan Nayur is a partner within McKinsey and part of the QuantumBlack, AI by McKinsey leadership team. He predominantly focuses on helping organizations build capabilities to industrialize and scale artificial intelligence (AI), including the newer Generative AI. He helps companies navigate innovations, technologies, processes, and digital skills as needed to run at scale. He is a keynote speaker and is recognized in the DataIQ 100 - a list of the top 100 influential people in data. Nayur also leads the firm’s diversity and inclusion efforts within QuantumBlack to promote a more equitable environment for all. He speaks with organizations on the importance of diversity and diverse team building—especially when working with data and building AI. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ilona on LinkedIn: www.linkedin.com/in/ilonalogvinova Connect with Mo on LinkedIn: https://www.linkedin.com/in/mabusaid/ Connect with Nayur on LinkedIn: https://www.linkedin.com/in/nayur/

Feb 02, 202456:20
Micro Graph Transformer Powering Small Language Models // Jon Cooke // #208

Micro Graph Transformer Powering Small Language Models // Jon Cooke // #208

Jon Cooke is the owner/founder of Dataception a Data, Analytics, and Data Product company, and the creator of the Data Product Pyramid, an adaptive Data Product operating model.

MLOps podcast #208 with Jon Cooke, CTO of Dataception, Micro Graph Transformer - Specialist Small Language Models Using Graphs to Accelerate Data Product Eco-systems. // Abstract Specialist deconstructed Encoder/Decoder Transformers along with data product management and tech to vastly accelerate prototyping, building, and deploying business-facing data products at high speed and low cost. // Bio Jon is a 20-year veteran in Data, Analytics, and AI and is a Data product specialist. After many times seeing the massive time, friction, failures, and costs typically associated with data and analytics initiatives, Jon founded Dataception. Its mission is to use tech to eliminate the data grunt and work together with data product management and AI to build and iterate sophisticated, business-facing analytics in real-time in front of the business. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI in Production Conference: https://home.mlops.community/public/events/ai-in-production-2024-02-15 Website: www.dataception.com https://www.linkedin.com/events/generativeai-dataproductsandbus7114951387100184576/theater/ https://www.linkedin.com/events/12thevalueofadataproductmanagem7110920848416366594/comments/ https://www.linkedin.com/events/howtoactuallyusedataproductstod7113570339535638528/theater/Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113: https://www.youtube.com/watch?v=JxVS3-4wyKc --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jon on LinkedIn: https://www.linkedin.com/in/jon-cooke-096bb0/

Timestamps: [00:00] AI in Production Conference teaser [02:12] Jon's preferred coffee [02:24] Takeaways [03:48] Please like, share, and subscribe to our MLOps channels! [04:02] Backpacking, traveling, and almost cast for Lord of the Rings [06:40] Jon's tech background [11:07] Dataception [15:05] Data Challenges: Delays & Causes [16:46] Data Virtualization for Agility [19:47] Large Company Change Challenges [21:28] Sales Tools Migration Challenges [24:44] Data and ML Integration [28:13] Data Roles Evolution [32:20] Tech for Prototyping Acceleration [35:22] LLM Enables Natural Language Data Analytics [36:36] Ensuring Reliable AI Information [38:20] Proxy Routing and Intelligent Agents [42:41] Human API for Data [46:49] Engineer Success with Growth [48:15] Tech CEO Balancing Act [53:59] Iterative Development for Product-Market Fit [56:16] Wrap up

Jan 30, 202457:11
How Data Platforms Affect ML & AI // Jake Watson // #207

How Data Platforms Affect ML & AI // Jake Watson // #207

Jake Watson is the writer of thedataplatform.substack.com⁠ and Principal Data Engineer at The Oakland Group.

MLOps podcast #207 with Jake Watson, Principal Data Engineer at The Oakland Group, How Data Platforms Affect ML & AI. // Abstract I’ve always told my clients and colleagues that traditional rule-based software is difficult, but software containing Artificial Intelligence (AI) and/or Machine Learning (ML)* is even more difficult, sometimes impossible. Why is this the case? Well, software is difficult because it’s like flying a plane while building it at the same time, but because AI and ML make rules on the fly based on various factors like training data, it’s like trying to build a plane in flight, but some parts of the plane will be designed by a machine, and you have little idea what that is going to look like till the machine finishes. This double goes for more cutting-edge AI models like GPT, where only the creators of the software have a vague idea of what it will output. This makes software with AI / ML more of a scientific experiment than engineering, which is going to make your project manager lose their mind when you have little idea how long a task is going to take. But what will make everyone’s lives easier is having solid data foundations to work from. Learn to walk before running. // Bio Jake has been working in data as an Analyst, Engineer, and/or Architect for over 10 years. Started as an analyst in the UK National Health Service converting spreadsheets to databases tracking surgical instruments. Then continued as an analyst at a consultancy (Capita) reporting on employee engagement in the NHS and dozens of UK Universities. There Jake moved reporting from Excel and Access to SQL Server, Python with frontend websites in d3.js. At Oakland Group, a data consultancy, Jake worked as a Cloud Engineer, Data Engineer, Tech Lead, and Architect depending on the project for dozens of clients both big and small (mostly big). Jake has also developed and productionised ML solutions as well in the NLP and classification space. Jake has experience in building Data Platforms in Azure, AWS, and GCP (though mostly in Azure and AWS) using Infrastructure as Code and DevOps/DataOps/MLOps. In the last year, Jake has been writing articles and newsletters for my blog, including a guide on how to build a data platform: https://thedataplatform.substack.com/p/how-to-build-a-data-platform // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://thedataplatform.substack.com/ How Data Platform Foundations Impact AI and ML Applications blog: https://thedataplatform.substack.com/p/issue-29-how-data-platform-foundations AI in Production Conference: https://home.mlops.community/public/events/ai-in-production-2024-02-15 How to Build a Data Platform blog: https://thedataplatform.substack.com/p/how-to-build-a-data-platform --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jake on LinkedIn: https://www.linkedin.com/in/jake-watson-data/ Timestamps: [00:00] Jake's preferred coffee [00:26] AI in Production Conference teaser [02:38] Takeaways [04:00] Please like, share, and subscribe to our MLOps channels! [04:17] Data Engineer's Crucial Role [05:44] Jake's background [06:44] Data Platform Foundations blog [10:34] Data mesh organizational side of things [17:58] Importance of data modeling [20:13] Dealing with the sprawl [22:03] Data quality [23:59] Data hierarchy on building a platform [29:34] ML Platform Team Structure [31:47] Don't reinvent the wheel [34:04] Data pipelines synergy [37:31] Wrap up

Jan 26, 202439:11
RAG Has Been Oversimplified // Yujian Tang // #206

RAG Has Been Oversimplified // Yujian Tang // #206

Yujian is working as a Developer Advocate at Zilliz, where they develop and write tutorials for proof of concepts for large language model applications. They also give talks on vector databases, LLM Apps, semantic search, and tangential spaces.

MLOps podcast #206 with Yujian Tang, Developer Advocate at Zilliz, RAG Has Been Oversimplified, brought to us by our Premium Brand Partner, Zilliz // Abstract In the world of development, Retrieval Augmented Generation (RAG) has often been oversimplified. Despite the industry's push, the practical application of RAG reveals complexities beyond its apparent simplicity. This talk delves into the nuanced challenges and considerations developers encounter when working with RAG, providing a candid exploration of the intricacies often overlooked in the broader narrative. // Bio Yujian Tang is a Developer Advocate at Zilliz. He has a background as a software engineer working on AutoML at Amazon. Yujian studied Computer Science, Statistics, and Neuroscience with research papers published to conferences including IEEE Big Data. He enjoys drinking bubble tea, spending time with family, and being near water. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: zilliz.com --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Yujian on LinkedIn: linkedin.com/in/yujiantang Timestamps: [00:00] Yujian's preferred coffee [00:17] Takeaways [02:42] Please like, share, and subscribe to our MLOps channels! [02:55] The hero of the LLM space [05:42] Embeddings into Vector databases [09:15] What is large and what is small LLM consensus [10:10] QA Bot behind the scenes [13:59] Fun fact getting more context [17:05] RAGs eliminate the ability of LLMs to hallucinate [18:50] Critical part of the rag stack [19:57] Building citations [20:48] Difference between context and relevance [26:11] Missing prompt tooling [27:46] Similarity search [29:54] RAG Optimization [33:03] Interacting with LLMs and tradeoffs [35:22] RAGs not suited for [39:33] Fashion App [42:43] Multimodel Rags vs LLM RAGs [44:18] Multimodel use cases [46:50] Video citations [47:31] Wrap up

Jan 23, 202448:56
The Myth of AI Breakthroughs // Jonathan Frankle // #205

The Myth of AI Breakthroughs // Jonathan Frankle // #205

Jonathan Frankle works as Chief Scientist (Neural Networks) at MosaicML (recently acquired by Databricks), a startup dedicated to making it easy and cost-effective for anyone to train large-scale, state-of-the-art neural networks. He leads the research team. MLOps podcast #205 with Jonathan Frankle, Chief Scientist (Neural Networks) at Databricks, The Myth of AI Breakthroughs, co-hosted by Denny Lee, brought to us by our Premium Brand Partner, Databricks. // Abstract Jonathan takes us behind the scenes of the rigorous work they undertake to test new knowledge in AI and to create effective and efficient model training tools. With a knack for cutting through the hype, Jonathan focuses on the realities and usefulness of AI and its application. We delve into issues such as face recognition systems, the 'lottery ticket hypothesis,' and robust decision-making protocols for training models. Our discussion extends into Jonathan's interesting move into the world of law as an adjunct professor, the need for healthy scientific discourse, his experience with GPUs, and the amusing claim of a revolutionary algorithm called Qstar. // Bio Jonathan Frankle is Chief Scientist (Neural Networks) at Databricks, where he leads the research team toward the goal of developing more efficient algorithms for training neural networks. He arrived via Databricks’ $1.3B acquisition of MosaicML as part of the founding team. He recently completed his PhD at MIT, where he empirically studied deep learning with Prof. Michael Carbin, specifically the properties of sparse networks that allow them to train effectively (his "Lottery Ticket Hypothesis" - ICLR 2019 Best Paper). In addition to his technical work, he is actively involved in policymaking around challenges related to machine learning. He earned his BSE and MSE in computer science at Princeton and has previously spent time at Google Brain and Facebook AI Research as an intern and Georgetown Law as an Adjunct Professor of Law. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.jfrankle.com Facial recognition: perpetuallineup.orgThe Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networksby Jonathan Frankle and Michael Carbin paper: https://arxiv.org/abs/1803.03635 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Denny on LinkedIn: https://linkedin.com/in/dennyglee Connect with Jonathan on LinkedIn: https://www.linkedin.com/in/jfrankle/ Timestamps: [00:00] Jonathan's preferred coffee [01:16] Takeaways [07:19] LM Avalanche Panel Surprise [10:07] Adjunct Professor of Law [12:59] Low facial recognition accuracy [14:22] Automated decision making human in the loop argument [16:09] Control vs. Outsourcing Concerns [18:02] perpetuallineup.org [23:41] Face Recognition Challenges [26:18] The lottery ticket hypothesis [29:20] Mosaic Role: Model Expertise [31:40] Expertise Integration in Training [38:19] SLURM opinions [41:30] GPU Affinity [45:04] Breakthroughs with QStar [49:52] Deciphering the noise advice [53:07] Real Conversations [55:47] How to cut through the noise [1:00:12] Research Iterations and Timelines [1:02:30] User Interests, Model Limits [1:06:18] Debugability [1:08:00] Wrap up

Jan 19, 202401:10:02
MLOps at the Crossroads // Patrick Barker & Farhood Etaati // #204

MLOps at the Crossroads // Patrick Barker & Farhood Etaati // #204

Patrick Barker is the Founder / CTO of Kentauros AI.

Farhood Etaati is a Software Engineer at Yektanet. MLOps podcast #204 with Patrick Barker, CTO of Kentauros AI and Farhood Etaati, MLOps/Platform Team Lead at AIMedic, MLOps at the Crossroads. // Abstract MLOps is at a crossroads. The ever-increasing excitement for LLMs' ability to solve some interesting real-world problems has made many people interested in applying these models in new applications which comes with its own challenges, that have upstarted the term "LLMLOps". But how much of those challenges are not a newer representation of what older-gen ML models had to deal with in the production, and the question arises whether developing "new" specialized tools to address these applications actually provides any substantial value for the sustainability of the field in general. Tools are coming and going at a rate that makes many technical people skeptical of adopting newer tools. What can we do as a community to alleviate these issues? Why OSS MLOps is lacking behind and how VC money is contributing to that? // Bio Farhood Etaati MLOps engineer at AIMedic. Studied EE at Uni of Tehran, started out as a data scientist, and pivoted to software engineering. Currently working on on-premise MLOps platform development suitable for Iran's infrastructure. Patrick Barker When Patrick is not occupied with building his AI company, he enjoys spending time with his wonderful kids or exploring the hills of Boulder. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Websites: https://github.com/pbarker

--------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Patrick on LinkedIn: https://www.linkedin.com/in/patrickbarkerco/ Connect with Farhood on LinkedIn: www.linkedin.com/in/farhood-etaati Timestamps: [00:00] Farhood's and Patrick's preferred coffee [01:13] Takeaways [04:00] Please like, share, and subscribe to our MLOps channels! [05:26] Strong feelings [10:21] MLOps vs DevOps Challenges [13:44] Medical setting, ML tools, NLP, model building [16:23] MLOps vs Data Engineering [20:45] MLOps Boosts LLM Development [23:54] Longtail Use Cases [31:00] Tech Roles Distinctions [34:42] Did He Say That? [37:04] Fine-tuning AI Models [38:57] ML 2.0 Advancements Explained [41:11] Generative AI in MLOps [45:04] ML Reproducibility Challenges [48:03] Wrap up

Jan 16, 202449:01
Pioneering AI Models for Regional Languages // Aleksa Gordić // #203

Pioneering AI Models for Regional Languages // Aleksa Gordić // #203

Aleksa Gordić is an ex-Google DeepMind / Microsoft ML engineer currently working on non-English LLMs at OrtusAI, open-sourcing Meta's NLLB (no language left behind) project and YugoGPT.

MLOps podcast #203 with Aleksa Gordić, Founder of OrtusAI, Pioneering AI Models for Regional Languages. // Abstract Dive deep into Aleksa's work with the YugoGPT, a language model serving Serbian, Croatian, Bosnian, and Montenegrin dialects - emphasizing the need for multilingual AI developments. Explore the unique language dynamics in the Balkans and Eastern Europe, the potential business opportunities around multilingual models, and the challenges in deploying large language models. Aleksa shares his experience with vision and image models, his collaborations with key tech players, and his use of advanced technologies. Hear about Aleksa Gordić's journey of being active and visible in the tech community and his insights into the world of machine learning and AI. Prepare to have your thinking challenged and horizons widened as we converse about the intriguing and complex world of MLOps. // Bio Working on non-English LLMs at OrtusAI, open-sourcing Meta's NLLB (no language left behind) project. Worked at DeepMind on the Flamingo project as a research engineer. Worked at Microsoft on the HoloLens 2 project & next-gen mixed reality glasses. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://gordicaleksa.com/ https://github.com/gordicaleksa - I build stuff :) https://discord.com/invite/peBrCpheKE - active AI Discord server (~6000) I bring the best AI researchers in the world to give talks (James Betker DALL-E 3 author, Tri Dao (Flash Attention), etc.) https://gordicaleksa.medium.com/how-i-got-a-job-at-deepmind-as-a-research-engineer-without-a-machine-learning-degree-1a45f2a781de - how I landed a job at DeepMind (and a couple more potentially interesting writings) Aleksa Gordić The AI Epiphany Youtube Channel: https://www.youtube.com/channel/UCj8shE7aIn4Yawwbo2FceCQ/videos W&B AI Academy: http://wandb.me/mlops_com_llm_course ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aleksa on LinkedIn: https://www.linkedin.com/in/aleksagordic/ Timestamps: [00:00] Aleksa's preferred coffee [00:17] Takeaways [02:51] Humming the GPU's [06:23] Built Chrome extension for communicating with videos [08:04] Rig Doubles Throughput Time [09:32] Vector databases advise [10:38] Learning from experts, connecting, and gathering insights. [13:47] Zero to Hero for MLOps [15:37] Serendipitous moments [17:52] Depth Over Breaking News [19:50] Trust in GPT Content [22:22] Exam Challenges and AI [26:53] YugoGPT [31:41] WandB Ad [33:33] Linguistic Mysteries [34:52] No Language Left Behind project (NLLB project) [36:53] YugoGPT Development Overview [37:49] NLLB vs YugoGPT [39:35] Yugo GPT parameters [41:16] Opportunities for unsupported languages [43:08] Diffusion model [44:39] Generative AI with image generation models [47:45] AI Challenges and Excitement [50:32] Challenges in different alphabet characters [52:10] Need a co-founder [56:05] Career transition and entrepreneurial mindset [1:00:20] Big Tech salary misconceptions [1:03:02] Inspiring wrap up

Jan 12, 202401:04:23
Small Data, Big Impact: The Story Behind DuckDB // Hannes Mühleisen & Jordan Tigani // #202

Small Data, Big Impact: The Story Behind DuckDB // Hannes Mühleisen & Jordan Tigani // #202

Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs. Jordan is co-founder and chief duck-herder at MotherDuck, a startup building a serverless analytics platform based on DuckDB.

MLOps podcast #202 with Hannes Mühleisen, Co-Founder & CEO of DuckDB Labs and Jordan Tigani, Chief Duck-Herder at MotherDuck, Small Data, Big Impact: The Story Behind DuckDB. // Abstract Navigate the intricacies of data management with Jordan Tagani and Hannes Mühleisen, the creative geniuses behind DuckDB and MotherDuck. This deep dive unravels the game-changing principles behind DuckDB's creation, tackling the prevailing wisdom to passionately fill the gap for smaller data set management. Let's also discover MotherDuck's unique focus on providing an unprecedented developer experience and its innovative edge in visualization and data delivery. This episode is teeming with enlightening discussions about managing community feedback, funding, and future possibilities that should not be missed for any tech enthusiasts and data management practitioners. // Bio Hannes Mühleisen Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs, a consulting company providing services around DuckDB. Hannes is also Professor of Data Engineering at Radboud Universiteit Nijmegen. His' main interest is analytical data management systems. Jordan Tigani Jordan is co-founder and chief duck-herder at MotherDuck, a startup building a serverless analytics platform based on DuckDB. He spent a decade working on Google BigQuery, as a founding engineer, book author, engineering leader, and product leader. More recently, as SingleStore’s Chief Product Officer, Jordan helped them build a cloud-native SaaS business. Jordan has also worked at Microsoft Research, the Windows Kernel team, and at a handful of star-crossed startups. His biggest claim to fame is predicting world cup matches using machine learning with a better record than Paul the Octopus. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Websites: https://duckdb.org/ https://motherduck.com/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Hannes on LinkedIn: https://www.linkedin.com/in/hfmuehleisen/ Connect with Jordan on LinkedIn: https://www.linkedin.com/in/jordantigani/ Timestamps: [00:00] Hannes and Jordan's preferred coffee [01:30] Takeaways [03:43] Swaggers in the house! [07:13] Duck DB's inception [09:38] Jordan's background [12:28] Simplify Developer Experience [17:54] Big Data Shift [26:01] Creation of MotherDuck [30:58] Duck DB and MotherDuck Partnership [31:57] Incentive Alignment Concerns [37:46] Building an incredible developer experience [43:38] User Testing Lab [47:18] Setting a higher standard [49:22] The moments before the moment [52:18] Gathering feedback and talking to the community [54:30] MotherDuck Features [1:00:19] Cloud Innovation for MotherDuck [1:02:41] ML Engineers and DuckDB [1:08:03] Wrap up

Jan 09, 202401:08:35
Language, Graphs, and AI in Industry // Paco Nathan // #201

Language, Graphs, and AI in Industry // Paco Nathan // #201

Paco Nathan is the Managing Partner at Derwen, Inc., and author of Latent Space, along with other books, plus popular videos and tutorials about machine learning, natural language, graph technologies, and related topics.

MLOps podcast #201 with Paco Nathan, Managing Partner at Derwen, Inc., Language, Graphs, and AI in Industry. // Abstract Let's talk about key findings from these conferences, specifically summarizing teams that have ROI on machine learning in production: what are the things in common they're doing, and what are the most important caveats they urge other teams to consider when getting started? Because these key takeaways aren't found in the current AI news cycle. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Conference: https://aiconference.com/ K1st World: https://www.k1st.world/ Corunna Innovation Summit: https://corunna.dataspartan.com/ "Cloud Computing on Amazon AWS EC2" UC Berkeley EECS guest lecture (2009) https://vimeo.com/manage/videos/3616394 "Hardware - Software - Process: Data Science in a Post-Moore’s Law World" https://www.nvidia.com/en-us/ai-data-science/resources/hardware-software-process-book/ “LLMs in Production: Learning from Experience” by Waleed Kadous @ Anyscale https://www.youtube.com/watch?v=xa7k9MUeIdk "Supercharging Industrial Operations with Problem-Solving GenAI & Domain Knowledge" by Christopher Nguyen @ Aitomatic https://www.k1st.world/2023-program/supercharging-industrial-operations-with-problem-solving-genai-domain-knowledge “The Next Million AI Systems” by Mark Huang @ Gradient: https://www.youtube.com/watch?v=lA0Npe4PqFw "AI in a Box" by Useful Sensors https://usefulsensors.com/#products "Opportunities in AI - 2023" by Andrew Ng https://www.youtube.com/watch?v=5p248yoa3oE "Advancing the Marine Industry Through the Harmony of Fishermen Knowledge and Al" by Akinori Kasai @ Furuno https://www.k1st.world/2023-program/advancing-the-marine-industry-through-the-harmony-of-fishermen-knowledge-and-al Macy conferences (1941-1960) https://en.wikipedia.org/wiki/Macy_conferences https://www.asc-cybernetics.org/foundations/history/MacySummary.htm https://press.uchicago.edu/ucp/books/book/distributed/C/bo23348570.html second-order cybernetics https://pangaro.com/designconversation/wp-content/uploads/dubberly-pangaro-chk-journal-2015.pdf https://en.wikipedia.org/wiki/Second-order_cybernetics Project Cybersyn https://jacobin.com/2015/04/allende-chile-beer-medina-cybersyn/ https://thereader.mitpress.mit.edu/project-cybersyn-chiles-radical-experiment-in-cybernetic-socialism/ https://99percentinvisible.org/episode/project-cybersyn/ https://medium.com/@rjog/project-cybersyn-an-early-attempt-at-iot-governance-and-how-we-can-apply-its-learnings-5164be850413 https://www.sustema.com/post/project-cybersyn-how-a-chilean-government-almost-controlled-the-economy-from-a-control-room https://transform-social.org/en/texts/cybersyn/ Humberto Maturana, Francisco Varela: Autopoeisis "De Maquinas y Seres Vivos" "Everything said is said by an observer" https://proyectos.yura.website/wp-content/uploads/2021/06/de_maquinas_y_seres_vivos_-_maturana.pdf https://en.wikipedia.org/wiki/Autopoiesis_and_Cognition:_The_Realization_of_the_Living Fernando Flores (led Project Cybersyn, imprisoned, later worked with Prof. Terry Winograd @ Stanford, the grad advisor for what became Google) https://lorenabarba.com/gallery/prof-barba-gave-keynote-at-pycon-2016/ https://conversationsforaction.com/fernando-flores "Navigating the Risk Landscape: A Deep Dive into Generative AI" by Ben Lorica and Andrew Burt https://thedataexchange.media/mitigating-generative-ai-risks/ "SpanMarker" by Tom Aarsen @ Hugging Face https://tomaarsen.github.io/SpanMarkerNER/ Examples of "the math catching up with the machine learning": Guy Van den Broeck @ UCLA https://web.cs.ucla.edu/~guyvdb/talks/ Charles Martin @ Calculations Consulting https://weightwatcher.ai/

Jan 05, 202401:18:29
Founding, Funding, and the Future of MLOps // Mihail Eric // #200

Founding, Funding, and the Future of MLOps // Mihail Eric // #200

Mihail Eric is an engineer, researcher, and educator who has helped start teams at innovative organizations such as Amazon Alexa and RideOS.

Mihail is a cofounder of Storia AI where they build an AI-powered creative assistant for fast and delightful image and video generation.

MLOps podcast #200 with Mihail Eric, Co-founder of Storia AI, Founding, Funding, and the Future of MLOps. // Abstract Demetrios and Mihail journey deep into the significance of human sentiment in an increasingly AI-driven era, the perils and promises of conversational AI, and the evolution and impact of image generation models. Delve into the world of MLOps versus LLMOps, offering clarifying perspectives on how the core concerns and technology persist, even amidst an evolving tech landscape with new buzzwords making waves. Mihail generously provides an inside look at his AI tool and its wide range of applications across various industries, offering insights on interesting niche-specific verticals and unexpected use cases. // Bio Mihail is a co-CEO of Storia AI, an early-stage startup building an AI-powered creative assistant for video production. He has over a decade of experience researching and engineering AI systems at scale. Previously he built the first deep-learning dialogue systems at the Stanford NLP group. He was also a founding member of Amazon Alexa’s first special projects team where he built the organization’s earliest large language models. Mihail is a serial entrepreneur who previously founded Confetti AI, a machine-learning education company that he led until its acquisition in 2022. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.mihaileric.com

https://www.storia.ai/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Timestamps: [00:00] Spotify playlist [02:08] How to live a longer healthier life [03:43] Absurd sweater collection [06:03] The catch-up episode [08:10] MLOps versus LLMOps [13:27] AI apps mixing visuals and text [16:00] Founder dating [16:54] Stable diffusion and difficulties with Mid Journey [23:12] Stripe developer experience [25:04] APIs and Model Providers [27:33] Host stable diffusion on AWS [34:07] AI Creativity: Prompt Experimentation [35:45] AI Challenges and Solutions [39:51] AI Hype Frustration [44:31] AI Impact on Hollywood [48:11] AI Impact on Filmmaking [51:48] Generalizable Tool for Verticals [52:49] MLOps versus LLMOps [56:42] Wrap up

Jan 02, 202457:31
Challenges Operationalizing ML (And Some Solutions) // Nathan Ryan Frank // #199

Challenges Operationalizing ML (And Some Solutions) // Nathan Ryan Frank // #199

Nathan Ryan Frank is the Machine Learning Operations and platform Director of Grainger. Former Astrophysicist turned data scientist and machine learning engineer with a proven history of delivering results into production across a wide variety of domains while leading projects with international, cross-functional teams. MLOps podcast #199 with Nathan Ryan Frank, Director, Machine Learning Platform & Operations at WW Grainger, Challenges Operationalizing Machine Learning (And Some Solutions). // Abstract This talk details some common challenges and pitfalls when attempting to operationalize machine learning systems and discusses some simple solutions. We dive into the machine learning development workflow and cover topics such as team dynamics, communication issues between roles that don't share a common language, and approaching MLOps from an SRE/DevOps perspective. Similarly, the talk highlights some challenges unique to operationalizing machine learning, drawing distinctions where necessary to highlight a large amount of similarity. Finally, the talk offers some simple and practical guidance for those new to MLOps who want to understand where to start and how to adopt best practices in an evolving field. // Bio Nathan Frank is currently the Director of Machine Learning Platform and Operations at Grainger where he is building a team to support the Technology Group's expanding machine learning efforts. Prior to joining Grainger, Nathan led machine learning engineering efforts at Strong Analytics, a boutique data science and machine learning consulting firm, as well as machine learning platform and development teams at Stats Perform, a leader in sports data and technology. Nathan holds bachelor's and master's degrees in Astrophysics from UC - Santa Cruz and UNC-Chapel Hill, respectively. When not building machine learning systems, Nathan spends as much time as possible with his favorite person in the world, his wife, as well as their four kids and two dogs, and enjoys getting outside to hike or garden and baking bread. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://nrfrank.github.io/ Bisi: https://bisi.gitbook.io/bisi/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nathan on LinkedIn: https://www.linkedin.com/in/nrfrank Timestamps: [00:00] Nathan's preferred coffee [00:40] Takeaways [02:00] Please leave a review in our comment sections! Please like, share, and subscribe to our MLOps channels! [03:00] Telescope for gamma-ray burst [07:31] Transition into ML [11:23] Stats-heavy US sports commentary [14:25] Building machine learning systems approach [20:02] ML Workflow Must-Haves [26:50] Love for tests [33:10] Test Writing Importance [34:37] Bridging Stakeholder Language Gap [43:04] Shared Language, Team Collaboration [47:28] Rapid fire questions [51:20] Wrap up

Dec 29, 202352:27
Inferring Creativity // Nick Hasty // #198

Inferring Creativity // Nick Hasty // #198

Nick Hasty is the Director of Product, Discovery & Machine Learning at Giphy, an animated-gif search engine that allows users to search, share, and discover GIFs.

MLOps podcast #198, Inferring Creativity. // Abstract Generative AI models have captured our imaginations with their ability to produce new "creative" works such as visually striking images, poems, stories, etc, and their outputs often rival or excel what most humans can do. I believe that these developments should make us re-think the nature of creativity itself, and through identifying parallels and differences between generative models and the human brain we can establish a framework to talk about creativity, and its relationship to intelligence, that should hold up against future revelations in ML and neuroscience. // Bio Nick Hasty is a technologist & entrepreneur with a background in the creative arts. He was the founding engineer for GIPHY, where he’s worked for the last 10 years and now leads ML/AI product initiatives. He also servers as a consultant helping early-stage startups scale their product and engineering teams. Before GIPHY, he worked with arts+cultural organizations such as Rhizome.org and the Alan Lomax archives. He got his graduate degree from NYU’s ITP program. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: http://jnhasty.com/ Previous talks: https://engineering.giphy.com/giphy2vec-natural-language-processing-giphy/ https://changelog.com/practicalai/38 ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nick on LinkedIn: https://www.linkedin.com/in/nickhasty/ Timestamps: [00:00] Nick's preferred coffee [00:15] Takeaways [06:58] Nick's background in ML [12:15] Nick's GIPHY journey [17:39] Nick's Success Factors [20:50] The trajectory of AI [28:09] Identifying as a product engineer [32:42] Evaluate LLMs vs. Traditional Models [35:03] AI Product: Intuition vs Data [38:53] Giphy AI Product Development [45:25] Startups and Venture Assistance [52:30] AI Funding Landscape Shift [54:00] Wrap up

Dec 26, 202355:30
The Role of Infrastructure in ML // Niels Bantilan // #197

The Role of Infrastructure in ML // Niels Bantilan // #197

MLOps podcast #197 with Niels Bantilan, Chief Machine Learning Engineer at Union, The Role of Infrastructure in ML Leveraging Open Source brought to us by Union. // Abstract When we start out building and deploying models in a new organization, life is simple: all I need to do is grab some data, iterate on a model that fits the data well and performs reasonably well on some held-out test set. Then, if you’re fortunate enough to get to the point where you want to deploy it, it’s fairly straightforward to wrap it in an app framework and host it on a cloud server. However, once you get past this stage, you’re likely to find yourself needing: More scalable data processing framework Experiment tracking for models Heavier duty CPU/GPU hardware Versioning tools to link models, data, code, and resource requirements Monitoring tools for tracking data and model quality There’s a rich ecosystem of open-source tools that solves each of these problems and more: but how do you unify all of them together into a single view? This is where orchestration tools like Flyte can help. Flyte not only allows you to compose data and ML pipelines, but it also serves as “infrastructure as code” so that you can leverage the open-source ecosystem and unify purpose-built tools for different parts of the ML lifecycle on a single platform. ML systems are not just models: they are the models, data, and infrastructure combined. // Bio Niels is the Chief Machine Learning Engineer at Union.ai, and core maintainer of Flyte, an open-source workflow orchestration tool, author of UnionML, an MLOps framework for machine learning microservices, and creator of Pandera, a statistical typing and data testing tool for scientific data containers. His mission is to help data science and machine learning practitioners be more productive. He has a Masters in Public Health with a specialization in sociomedical science and public health informatics, and prior to that a background in developmental biology and immunology. His research interests include reinforcement learning, AutoML, creative machine learning, and fairness, accountability, and transparency in automated systems. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://github.com/cosmicBboy, https://union.ai/Flyte: https://flyte.org/ MLOps vs ML Orchestration // Ketan Umare // MLOps Podcast #183 - https://youtu.be/k2QRNJXyzFg ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Niels on LinkedIn: https://www.linkedin.com/in/nbantilan/ Timestamps: [00:00] Niels' preferred coffee [00:17] Takeaways [03:45] Shout out to our Premium Brand Partner, Union! [04:30] Pandera [08:12] Creating a company [14:22] Injecting ML for Data [17:30] ML for Infrastructure Optimization [22:17] AI Implementation Challenges [24:25] Generative DevOps movement [28:27] Pushing Limits: Code Responsibility [29:46] Orchestration in OpenAI's Dev Day [34:27] MLOps Stack: Layers & Challenges [42:45] Mature Companies Embrace Kubernetes [45:29] Horizon Challenges [47:24] Flexible Integration for Resources [49:10] MLOps Reproducibility Challenges [53:14] MLOps Maturity Spectrum [57:48] First-Class Citizens in Design [1:00:16] Delegating for Efficient Collaboration [1:04:55] Wrap up

Dec 22, 202301:05:24
LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196

LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196

Laurel Orr is a Principal Engineer at Numbers Station, a startup that applies Foundation Model technology to the enterprise data stack. Venky Orr is SVP of Product & Engineering.

MLOps podcast #196 with Numbers Station's Venky Ganti SVP, Product & Engineering and Principal Engineer, Laurel Orr, LLMs in Focus: From One-Size Fits All to Verticalized Solutions. // Abstract Dive into the realm of large language models (LLMs) as we explore the merits and limitations of 'one-size fits all' LLMs, and their role in data analytics. Through customer stories, we showcase real-world applications and contrast general LLMs with verticalized, enterprise-centric models. We address the significance of ownership structures, with a focus on open-source vs proprietary impacts on transparency and trustworthiness. Delving into the NSQL foundation models, we emphasize the importance of diverse, quality training data, especially with enterprise challenges. Lastly, we speculate on the future of LLMs, highlighting hosting solutions and the evolution towards specialized challenges. // Bio Laurel Orr Laurel Orr is a Principal Engineer at Numbers Station, a startup that applies Foundation Model technology to the enterprise data stack. Her research interests include how to use FMs to solve classically hard data-wrangling tasks and how to put FM technology into deployment. Before Numbers Station, Laurel was a postdoc at Stanford advised by Chris Re as part of the Hazy Research Labs working in the intersection of AI and data management. She graduated with a PhD in database systems from the University of Washington. Venky Orr Venky brings over two decades of experience in software engineering and technical leadership to Numbers Station as SVP of Product & Engineering. Most recently, he served as General Manager leading several initiatives on query understanding and commerce in the ads product area at Google. Before that, he was CEO and co-founder of Mesh Dynamics, the API test automation company, which was acquired by Google in 2021. Prior to Mesh Dynamics, Venky was CTO and co-founder of Alation, the enterprise data catalog company, where he led technology and helped create the new data catalog product category. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.numbersstation.ai/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Laurel on LinkedIn: https://www.linkedin.com/in/laurel-orr/ Connect with Venky on LinkedIn: https://www.linkedin.com/in/venky-ganti-2679a2/ Timestamps: [00:00] Venky's and Laurel's preferred coffee [00:36] Takeaways [03:15] Please like, share, and subscribe to our MLOps channels! [04:38] Venky's background [07:47] Laurel's at background [09:38] Data wrangling [13:45] Sequel query [19:25] One size-fits-all LLMs vs Verticalized and Specific LLMs [23:42] Model Choice Trade-offs [30:18] NSQL Foundational Models [37:26] LLM Trends in 12 Months [40:09] Data recipes being democratized [45:16] Claude and 100,000 Context [48:02] Exploring Varieties of LLMs [50:02] AI Gateway [51:07] Text-to-SQL Model Evaluation [54:00] Wrap up

Dec 19, 202355:15
[Exclusive] Weights & Biases Round-table // Model Management in a Regulated Environment

[Exclusive] Weights & Biases Round-table // Model Management in a Regulated Environment

MLOps Coffee Sessions Special episode with Weights & Biases, Model Management in a Regulated Environment, fueled by our Premium Brand Partner, Weights & Biases. // Abstract Step into the fascinating world of Language Model Management (LLMs) in a Regulated Environment! Join us for an enlightening chat where we'll explore the intricacies of managing models within highly regulated settings, focusing on compliance and effective strategies. This is your opportunity to be part of a dynamic conversation that delves into the challenges and best practices of Model Management in Regulated Environments. Secure your spot today and stay tuned for an enriching dialogue on navigating the complexities of navigating the regulated terrain. Don't miss out on the chance to broaden your understanding and connect with peers in the field! // Bio Darek Kłeczek Darek Kłeczek is a Machine Learning Engineer at Weights & Biases, where he leads the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal, and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek contributed the first Polish versions of BERT and GPT language models and is a Kaggle Competitions Grandmaster. Mark Huang Mark is a co-founder and Chief Architect at Gradient, a platform that helps companies build custom AI applications by making it extremely easy to fine-tune foundational models and deploy them into production. Previously, he was a tech lead in machine learning teams at Splunk and Box, developing and deploying production systems for streaming analytics, personalization, and forecasting. Prior to his career in software development, he was an algorithmic trader at quantitative hedge funds where he also harnessed large-scale data to generate trading signals for billion-dollar asset portfolios. Oliver Chipperfield Oliver Chipperfield is a Senior Data Scientist and Team Lead at M-KOPA, where he utilizes his expertise in machine learning and data-driven innovation. At M-KOPA since October 2021, Oliver leads a diverse tech team, making improvements in credit loss forecasting and fraud detection. His career spans multiple industries, where he has applied his extensive knowledge in Python, Spark, R, SQL, and Excel. He also specialized in the building and design of production ML systems, experimentation, and Bayesian statistics. Michelle Marie Conway As an Irish woman who relocated to London after completing her university studies in Dublin, Michelle spent the past 12 years carving out a career in the data and tech industry. With a keen eye for detail and a passion for innovation, She has consistently leveraged my expertise to drive growth and deliver results for the companies she worked for. As a dynamic and driven professional, Michelle is always looking for new challenges and opportunities to learn and grow, and she's excited to see what the future holds in this exciting and ever-evolving industry. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Fine-Tuning LLMs: Best Practices and When to Go Small // Mark Kim-Huang // MLOps Meetup #124 - https://youtu.be/1WSUfWojoe0 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Darek on LinkedIn: https://www.linkedin.com/in/kleczek/ Connect with Mark on LinkedIn: https://www.linkedin.com/in/markhng525/ Connect with Oliver on LinkedIn: https://www.linkedin.com/in/oliver-chipperfield/ Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle-conway-40337432

Dec 15, 202358:30
Building the Future of AI in Software Development // Varun Mohan // #195

Building the Future of AI in Software Development // Varun Mohan // #195

MLOps podcast #195 with Varun Mohan, CEO of Codeium, Building the Future of AI in Software Development brought to us by QuantumBlack. // Abstract This brief overview traces the evolution of Exafunction and Codeium, highlighting the strategic transition. It explores the inception of Codeium's key features, offering insights into the thoughtful design process. This emphasizes the company's forward-looking approach to preparing for a rapidly advancing technological landscape. Additionally, it touches upon developing essential MLOps systems, showcasing the commitment to maintaining rigor and efficiency in the face of evolving challenges. // Bio Varun Mohan developed a knack for programming in high school where he actively participated in various competitions. This passion for programming was shared with his now co-founder, with whom he frequently competed. Their common interest in programming and competition led them to attend MIT together, where they undertook more programming challenges. After college, they ventured into the Bay Area where they continued to compete and further cultivate their programming abilities. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Websites: codeium.com, https://exafunction.com/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Varun on Twitter: https://www.linkedin.com/in/varunkmohan/ Timestamps: [00:00] Varun's preferred coffee [00:15] Takeaways [02:50] Please like, share, and subscribe to our MLOps channels! [03:05] QuantumBlack ad by Nayur Khan [05:51] Varun's background in tech [10:55] Language Models Advancement [14:17] GPU scarce world [18:23] Vision and Pain Points [19:18] Fine-tuning Challenges in NLP [21:04] ML and AI Caution [21:49] MLOps: App vs Infra [23:53] Data Engineering Abstraction Evolution [26:12] Codeium and Scaling Discussion [31:59] API, Cloud, Computation [34:20] Codeium scaling [35:11] Reserved GPUs, companies self-hosting products [38:00] Open-source code Codeium training [40:03] Protecting IP Licenses [41:32] ML Challenges: Data, Bias, Security [44:37] Evaluating code [48:29] Getting values from Codeium [49:49] Exafunction ML AI Production [52:17] AWS Creation [53:58] Feature flags and MA AI lifecycle [56:34] Coding problem [58:40] New software architectures [1:03:28] Wrap up

Dec 12, 202301:04:35
AI in Education Fireside Chat // LLMs in Production Conference 3

AI in Education Fireside Chat // LLMs in Production Conference 3

// Abstract Explore the transformative role of AI in EdTech, discussing its potential to enhance learning experiences and personalize education. The panelists share insights on AI use cases, challenges in AI integration, and strategies for building a differentiated business model in the evolving AI landscape. The discussion looks ahead at how the latest wave of GenAI is set to shape the future of education. Join us to understand the exciting prospects and challenges of AI in EdTech. Moderator: Paul van der Boor // Bio Klinton Bicknell Klinton Bicknell is the Head of AI  @duolingo . He works at the intersection of artificial intelligence and cognitive science. His research has been published in venues including ACL, PNAS, NAACL, Psychological Science, EDM, CogSci, and Cognition, and covered in the Financial Times, BBC, and Forbes. Prior to Duolingo, he was an assistant professor at Northwestern University. Bill Salak Bill Salak has more than 20 years of experience overseeing large-scale development projects and more than 24 years of experience in web application architecture and development. Bill founded and served as CTO of multiple Internet and web development companies, leading technology projects for companies including Age of Learning, AOL, Educational Testing Systems, Film LA, Hasbro, HBO, Highlights for Children, NBC-Universal, and the U.S. Army. Bill currently serves as the CTO of  @Brainly-app , the leading learning platform worldwide with the most extensive Knowledge Base for all school subjects and grades. Yeva Hyusyan Yeva Hyusyan is the Co-Founder and CEO of  @Sololearn , the most engaging platform for learning how to code. Prior to co-founding SoloLearn, Yeva established a startup accelerator for mobile games, consumer apps, and ag-tech solutions. In a previous role, she implemented programs for the World Bank and the US Government in business and education. Later, she served as a General Manager at Microsoft, where she led sales, developer ecosystem development, and strategic partnerships. Yeva holds an MBA in Corporate Strategy from Maastricht School of Management in the Netherlands, an MS in International Economics from Yerevan State University in Armenia, and completed the Executive Program at Stanford University's Graduate School of Business. // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

Dec 08, 202331:00
[Exclusive] Tecton Round-table // Get your ML Application Into Production

[Exclusive] Tecton Round-table // Get your ML Application Into Production

Join our conference: https://home.mlops.community/public/events/llms-in-production-part-iii-2023-10-03


MLOps Coffee Sessions Special episode with Tecton, Get your ML Application Into Production, sponsored by Tecton. // Abstract Getting an ML application into production is more difficult than most teams expect—but with the right preparation, it can be done efficiently! Join us for this exclusive roundtable, where 4 machine learning experts from Tecton will discuss some of the most common challenges and best practices to avoid them. With over 35 years of combined experience in MLOps at companies like AWS, Google, Lyft, and Uber, and 15 years of experience at Tecton spent helping customers like FanDuel, Plaid, and HelloFresh getting ML models into production, the presenters will share how factors like organizational structure, use cases, tech stack, and more, can create different types of bottlenecks. They’ll also share best practices and lessons learned throughout their careers on how to overcome these challenges. // Bio Kevin Stumpf Kevin co-founded Tecton where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years. Prior to Uber, Kevin founded Dispatcher, with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor's Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete. Derek Salama Derek is currently a Senior Product Manager at Tecton, where he is responsible for security, collaboration experience, and Feature Platform infrastructure. Prior to Tecton, Derek worked at Google and Lyft across both ML infrastructure and ML applications. Eddie Esquivel Eddie Esquivel is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for operational ML. Prior to Tecton, Eddie was a Solutions Architect at AWS. He holds a Bachelor’s Degree in Computer Science & Engineering from the University of California, Los Angeles. Isaac Cameron Isaac Cameron is a Consulting Architect at Tecton. Prior to Tecton, he was a Principal Solutions Architect at Slalom Build, focusing on data and machine learning, where he built his own feature platform for a large U.S. airline and has enabled many organizations to build intelligent products leveraging operational ML. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kevin on LinkedIn: https://www.linkedin.com/in/kevinstumpf/ Connect with Derek on LinkedIn: https://www.linkedin.com/in/dereksalama/ Connect with Eddie on LinkedIn: https://www.linkedin.com/in/eddie-esquivel-2016/ Connect with Isaac on LinkedIn: https://www.linkedin.com/in/isaaccameron/ Timestamps: [00:00] Introduction to Kevin Stumpf, Derek Salama, Eddie Esquivel, and Isaac Cameron [02:48] Challenges of traditional classical ML into production [10:21] Infrastructure cost [16:50] Bridging Business and Tech [19:23] ML Infrastructure Essentials [29:38] Integrated Batch and Stream [35:12] Scaling AI from Zero [36:23] Stacks red flags [45:53] Tecton: Features Quality Monitoring [49:06] Building Recommender System Tools [53:19] Quantify business value in ML [54:40] Wrap up

Dec 07, 202355:41
DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // #194

DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // #194

MLOps podcast #194 with Omar Khattab, PhD Candidate at Stanford, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines. // Abstract The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM pipelines are typically implemented using hard-coded "prompt templates", i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric. We conduct two case studies, showing that succinct DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting and pipelines with expert-created demonstrations. On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely on expert-written prompt chains for proprietary GPT-3.5. DSPy is available as open source at https://github.com/stanfordnlp/dspy // Bio Omar Khattab is a PhD candidate at Stanford and an Apple PhD Scholar in AI/ML. He builds retrieval models as well as retrieval-based NLP systems, which can leverage large text collections to craft knowledgeable responses efficiently and transparently. Omar is the author of the ColBERT retrieval model, which has been central to the development of the field of neural retrieval, and author of several of its derivate NLP systems like ColBERT-QA and Baleen. His recent work includes the DSPy framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://omarkhattab.com/ DSPy: https://github.com/stanfordnlp/dspy ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Omar on Twitter: https://twitter.com/lateinteraction Timestamps: [00:00] Omar's preferred coffee [00:26] Takeaways [06:40] Weight & Biases Ad [09:00] Omar's tech background [13:35] Evolution of RAG [16:33] Complex retrievals [21:32] Vector Encoding for Databases [23:50] BERT vs New Models [28:00] Resilient Pipelines: Design Principles [33:37] MLOps Workflow Challenges [36:15] Guiding LLMs for Tasks [37:40] Large Language Models: Usage and Costs [41:32] DSPy Breakdown [51:05] AI Compliance Roundtable [55:40] Fine-Tuning Frustrations and Solutions [57:27] Fine-Tuning Challenges in ML [1:00:55] Versatile GPT-3 in Agents [1:03:53] AI Focus: DSP and Retrieval [1:04:55] Commercialization plans [1:05:27] Wrap up

Dec 05, 202301:05:39
Fireside Chat with LLM Startups // LLMs in Production Conference 3

Fireside Chat with LLM Startups // LLMs in Production Conference 3

// Abstract Martian is focused on building a model router to dynamically route every prompt to the best LLM for the highest performance and lowest cost. Corti, the Al Co-Pilot for health care uses Al to improve patient care, demonstrating the potential of Al in healthcare and medical decision-making. They recently raised $60M, with Prosus being one of the lead investors. Transforms is pioneering in synthetic entertainment, showing how Al can transform the way we create and consume media. Moderator: Paul van der Boor // Speakers Sandeep Bakshi Head of Investments, Europe  @prosusgroup3707  Shriyash Upadhyay Founder @Martian Lars Maaløe Co-Founder & CTO at Corti | Adj. Assoc. Professor of Machine Learning @ Corti Pietro Gagliano President & Founder @Transitional Forms // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

Dec 01, 202331:12
LLMs in Biomaterials Production // Pierre Salvy // #193

LLMs in Biomaterials Production // Pierre Salvy // #193

MLOps podcast #193 with Pierre Salvy, Head of Engineering at Cambrium, LLM in Material Production co-hosted by Stephen Batifol. // Abstract Delve into the world of proteins, genetic engineering, and the intersection of AI and biotech. Pierre explains how his company is using advanced models to design proteins with specific properties, even creating a vegan collagen for cosmetics. By harnessing the potential of AI, they aim to revolutionize sustainability, uncovering a future of lab-grown meats, molecular cheese, and less harmful plastics, confronting regulatory barriers and decoding the syntax and grammar of proteins. // Bio Head of Engineering at Cambrium, a biotech company utilising genAI to design sustainable protein biomaterials for the future. Pierre spent the last decade researching ways to make computers calculate better biological systems. This is a critical step to engineering more sustainable ways to make the products we use every day, which is their mission at Cambrium. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: cambrium.bio --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Pierre on LinkedIn: https://www.linkedin.com/in/psalvy/ Timestamps: [00:00] Pierre's preferred coffee [00:10] Takeaways [05:10] Please like, share, and subscribe to our MLOps channels! [05:25] Weights and Biases ad [07:52] Ski story [09:54] Pierre's career trajectory [13:35] From employee #2 to hiring a team [14:42] From employee #2 to head of engineering [15:50] Uncomfortable things to say essential for growth and effectiveness [18:27] From biotech to engineering [21:10] LLMs at Cambrium [24:26] Slackbot [25:43] Quick and Easy Solutions [26:47] Products created at Cambrium [31:56] Impact of EU Regulation on Cambrium [35:39] 2nd Biotech Winter [36:35] Cost of error vs service not working [38:00] Protein Synthesis and Mutations [40:03] Large-Scale System Engineering Challenges [43:28] Expensive Factors in Experiments [44:39] LLMs vs Protein Models [47:03] Protein Design with LLMs [49:43] Eco-Friendly Product Vision [53:28] Space glue [54:00] Wrap up

Nov 28, 202354:60
Product Engineering for LLMs // LLMs in Production Conference Part III // Panel 2

Product Engineering for LLMs // LLMs in Production Conference Part III // Panel 2

// Abstract A product-minded engineering perspective on UX/design patterns, product evaluation, and building with AI. // Bio Charles Frye Charles teaches people how to build ML applications. After doing research in psychopharmacology and neurobiology, he pivoted to artificial neural networks and completed a PhD at the University of California, Berkeley in 2020. He then worked as an educator at Weights & Biases before joining @Full Stack Deep Learning, an online community and MOOC for building with ML. Sahar Mor Sahar is a Product Lead at  @stripe  with 15y of experience in product and engineering roles. At Stripe, he leads the adoption of LLMs and the Enhanced Issuer Network - a set of data partnerships with top banks to reduce payment fraud. Prior to Stripe he founded a document intelligence API company, was a founding PM in a couple of AI startups, including an accounting automation startup (Zeitgold, acq'd by Deel), and served in the elite intelligence unit 8200 in engineering roles. Sahar authors a weekly AI newsletter (AI Tidbits) and maintains a few open-source AI-related libraries (https://github.com/saharmor). Sarah Guo Sarah Guo is the Founder and Managing Partner at @Conviction, a venture capital firm founded in 2022 to invest in intelligent software, or "Software 3.0." Prior, she spent a decade as a General Partner at Greylock Partners. She has been an early investor or advisor to 40+ companies in software, fintech, security, infrastructure, fundamental research, and AI-native applications. Sarah is from Wisconsin, has four degrees from the University of Pennsylvania, and lives in the Bay Area with her husband and two daughters. She co-hosts the AI podcast "No Priors" with Elad Gil. Shyamala Prayaga Shyamala is a seasoned conversational AI expert. Having led initiatives across connected home, automotive, wearables - just to name a few, she's put her work on research into usability, accessibility, speech recognition, multimodal voice user interfaces, and has even been published internationally across publications like Forbes. Outside of her research, she's spent the last 18 years designing mobile, web, desktop, and smart TV interfaces and has most recently joined  @NVIDIA  to work on deep learning product suites. Willem Pienaar Willem is the creator of @Feast, the open-source feature store and a builder in the generative AI space. Previously Willem was an engineering manager at Tecton where he led teams in both their open source and enterprise initiatives. Before that Willem built the core ML systems and created the ML platform team at Gojek, the Indonesian decacorn. // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

Nov 24, 202331:46
Enterprises Using MLOps, the Changing LLM Landscape, MLOps Pipelines // Chris Van Pelt // #192

Enterprises Using MLOps, the Changing LLM Landscape, MLOps Pipelines // Chris Van Pelt // #192

MLOps podcast #192 with Chris Van Pelt, CISO and co-founder of Weights & Biases, Enterprises Using MLOps, the Changing LLM Landscape, MLOps Pipelines sponsored by  @WeightsBiases . // Abstract Chris, provides insights into his machine learning (ML) journey, emphasizing the significance of ML evaluation processes and the evolving landscape of MLOps. The conversation covers effective evaluation metrics, demo-driven development nuances, and the complexities of ML Ops pipelines. Chris reflects on his experience with Crowdflower, detailing its transition to Weights and Biases and stressing the early integration of security measures. The discussion extends to the transformative impact of ML on the tech industry, challenges in detecting subtle bugs, and the potential of open-source models and multimodal capabilities. // Bio Chris Van Pelt is a co-founder of Weights & Biases, a developer MLOps platform. In 2009, Chris founded Figure Eight/CrowdFlower. Over the past 12 years, Chris has dedicated his career optimizing ML workflows and teaching ML practitioners, making machine learning more accessible to all. Chris has worked as a studio artist, computer scientist, and web engineer. He studied both art and computer science at Hope College. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://wandb.ai/site ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisvanpelt/ Timestamps: [00:00] Chris' preferred coffee [00:33] Takeaways[03:50] Huge shout out to Weights & Biases for sponsoring this episode! [04:15] Please like, share, and subscribe to our MLOps channels! [04:25] CrowdFlower [07:02] Difference of CrowdFlower and Trajectory [09:13] Transition from CrowdFlower to Weights & Biases [13:05] Excel spreadsheets being passed around via email [15:45] Evolution of Weights & Biases [19:24] CISO role [22:23] Advise for easy wins [25:32] Transition into LLMs [27:36] Prompt injection risks on data [29:42] LLMs for New Personas [34:42] Iterative Value Evaluation Process [36:36] Iterating on New Release [39:31] Evaluation survey [43:21] Landscape of LLMs and its evolution [45:40] Conan O'Brien [46:48] Wrap up

Nov 21, 202347:50
Building Defensible AI Apps // Gregory Kamradt // #191

Building Defensible AI Apps // Gregory Kamradt // #191

MLOps podcast #191 with Gregory Kamradt, Founder of  @DataIndependent, Building Defensible AI Apps sponsored by  @MilvusVectorDatabase . // Abstract Demetrios engages in a captivating conversation with Gregory Kamradt, an AI visionary deeply immersed in technology and product development. The discussion spans various challenges businesses encounter in implementing AI, the transformative potential of AI in revolutionizing business processes, and the growth and possibilities associated with OpenAI. Gregory shares insights into his latest project, a smart companion app designed to analyze and summarize startup pitches. The episode unfolds as a rich source of knowledge, exploring diverse topics such as AI experimentation, the concept of an AI gateway, the future of finely tuned models for niche applications, and insights into the intricate landscape of AI within big tech, including Google's strategic direction and OpenAI's copyright protection measures. // Bio Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications. By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers. Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools. He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://gregkamradt.com/ Greg Kamradt (Data Indy): https://www.youtube.com/@DataIndependent Milvus Vector Database: https://zilliz.com/what-is-milvus ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkamradt/ Timestamps: [00:00] Greg's preferred coffee [00:12] Takeaways [02:56] Quick word from our sponsor [04:22] DevDay [06:19] YouTube's unique perspective on the technological revolution [09:34] GPT assistance [13:36] AI Streamlining Fax Orders [18:13] AI Marketplace Dynamics: GPT vs. Specialized [22:04] Data Tooling Platform Challenges [27:17] The Shield against copyright [29:27] Llama Index vs OpenAI [31:56] DS Pie and Compiler Tangent [34:31] Orchestration Layer is dead! [36:49] Personalized AI Models: Understanding Integration [38:00] AI Defensibility [43:00] Green Field AI Opportunities [46:57] LLMs for live event pitch [53:38] Exciting content creation process [58:03] New context window benchmark [1:02:23] AI Gateway [1:04:35] Wrap up

Nov 17, 202301:05:33
Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security // Ads Dawson // #190

Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security // Ads Dawson // #190

MLOps podcast #190 with Ads Dawson, Senior Security Engineer at Cohere, Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security. // Abstract Ads Dawson, a seasoned security engineer at Cohere, explores the challenges and solutions in securing large language models (LLMs) and natural language programming APIs. Drawing on his extensive experience, Ads discusses approaches to threat modeling LLM applications, preventing data breaches, defending against attacks, and bolstering the security of these critical technologies. The presentation also delves into the success of the "OWASP Top 10 for Large Language Model Applications" project, co-founded by Ads, which identifies key vulnerabilities in the industry. Notably, Ads owns three of the top 10 vulnerabilities, including Training Data Poisoning, Sensitive Information Disclosure, and Model Theft. This OWASP Top 10 serves as a foundational resource for stakeholders in AI, offering guidance on using, developing, and securing LLM applications. Additionally, the session covers insider news from the AI Village's 'Hack the Future' | LLM Red Teaming event at Defcon31, providing insights into the inaugural Generative AI Red Teaming showdown and its significance in addressing security and privacy concerns amid the widespread adoption of AI. // Bio A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics. Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection for example. Ads is also a networking veteran and love a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://github.com/GangGreenTemperTatum OWASP Top 10 for Large Language Model Applications Core Team Member and Founder - https://owasp.org/www-project-top-10-for-large-language-model-applications/CoreTeam Fork for OWASP Top 10 for Large Language Model Applications - https://github.com/GangGreenTemperTatum/www-project-top-10-for-large-language-model-applications Security project: llmtop10.com --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ads on LinkedIn: https://www.linkedin.com/in/adamdawson0/ Timestamps: [00:00] Ads' preferred coffee [00:46] Takeaways [02:52] Please like, share, and subscribe to our MLOps channels! [03:11] Security and vulnerabilities [05:24] Work at Cohere and OWASP [08:11] Previous work vs LLMs Companies [09:46] LLM vulnerabilities [10:38] Good qualities to combat prompt injection problems [13:26] Data lineage [16:03] Red teaming [19:39] Freakiest LLM vulnerabilities [22:17] Severe Autonomy Concerns [25:13] Hallucinations [27:59] Prompt injection [29:15] Vector attacks to be recognized [32:02] LLMs being customed [33:18] Security changes due to maturity [38:17] OWASP Top 10 for Large Language Model Applications [44:31] Gandalf game [46:06] Prompt injection attack [49:46] Overlapping security [53:26] Data poisoning [56:57] Toxic data for LLMs [58:50] Wrap up

Nov 14, 202359:41
Designing for Forward Compatibility in Gen AI // Rohit Agarwal // #189

Designing for Forward Compatibility in Gen AI // Rohit Agarwal // #189

MLOps podcast #189 with Rohit Agarwal, CEO of Portkey.ai, Designing for Forward Compatibility in Gen AI. // Abstract For two whole years of working with a large LLM deployment, I always felt uncomfortable. How is my system performing? Are my users liking the outputs? Who needs help? Probabilistic systems can make this really hard to understand. In this talk, we'll discuss practical & implementable items to secure your LLM system and gain confidence while deploying to production. // Bio Rohit is the Co-founder and CEO of portkey.ai which is an FMOps stack for monitoring, model management, compliance, and more. Previously, he headed Product & AI at Pepper Content which has served ~900M generations on LLMs in production. Having seen large LLM deployments in production, he's always happy to help companies build their infra stacks on FM APIs or Open-source models. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://portkey.ai ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Rohit on LinkedIn: https://www.linkedin.com/in/1rohitagarwal/ Timestamps: [00:00] Rohit's preferred coffee [00:15] Takeaways [03:22] Please like, share, and subscribe to our MLOps channels! [05:16] Rohit's current work [06:37] The Portkey landscape [09:13] Compute unit is no longer a Cloud resource, it's a Foundational Model [11:09] Hang-ups at high-scale models and how to combat them [15:22] Complexity of the Apps evolving [19:54] Rohit's working relationships with the agents [22:52] Fine-tuning reliability [24:38] Small language models can outperform larger ones [26:38] Market map at Portkey [34:37] AI Gateway [37:59] Worker Bee and Queen Bee [39:27] Security and Compliance [43:11] Idea of Data Mesh [45:57] Forward compatibility [49:59] Decoupling AI Gateway from the code [56:05] Hardest design decisions to make since creating Portkey [58:52] Wrap up

Nov 10, 202301:00:18
Impact of LLMs on the Tech Stack and Product Development // Anand Das // #188

Impact of LLMs on the Tech Stack and Product Development // Anand Das // #188

MLOps podcast #188 with Anand Das, Co-founder and CTO of Bito, Impact of LLMs on the Tech Stack and Product Development. // Abstract Anand and his team have developed a fascinating Chrome extension called "explain code" that has garnered significant attention in the tech community. They have expanded their extension to other platforms like Visual Studio code and Chat Brains, creating a personal assistant for code generation, explanation, and test case writing. // Bio Anand Das is the co-founder and CTO of Bito. Previously, he served as the CTO at Eyeota, which was acquired by Dun & Bradstreet for $165M in 2021. Anand also co-founded and served as the CTO of PubMatic in 2006, a company that went public on NASDAQ in 2020 (NASDAQ: PUBM). Anand has also held various engineering roles at Panta Systems, a high-performance computing startup led by the CTO of Veritas, as well as at Veritas and Symantec, where he worked on a variety of storage and backup products. Anand holds seven patents in systems software, storage software, advertising, and application software. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://bito.ai/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Anand on LinkedIn: https://www.linkedin.com/in/ananddas/ Timestamps: [00:00] Anand's preferred coffee [00:15] Takeaways [02:49] Please like, share, and subscribe to our MLOps channels! [03:08] Anand's tech background [10:06] Fun at Optimization Level [12:59] Trying all APIs [17:55] Models evaluation decision tree [22:51] Weights and Biases Ad [25:04] AI Stack that understands the code [28:27] Tools for the Guard Rails [33:23] Seeking solutions before presenting to LLM [38:46] Prompt-Driven Development Insights [40:16] Prompting best practices [42:51] Unneeded complexities [45:45] Cost-benefit analysis of buying GPUs [49:13] ML Build vs Buy [51:26] Best practices for debugging code assistant [54:58] Wrap up

Nov 07, 202354:50
Building Effective Products with GenAI // Faizaan Charania // #187

Building Effective Products with GenAI // Faizaan Charania // #187

MLOps podcast #187 with Faizaan Charania, Product Manager, AI at LinkedIn, Building Effective Products with GenAI. // Abstract Faizaan outlines his AI product development approach, starting broadly and refining details with tech leads, emphasizing the value of a simplified MVP. He also explores integrating generative AI, highlighting its role in enhancing user experiences through LLMs. In this discussion, Faizaan shares wisdom on feedback integration, user trust, and the collaboration challenges between product managers and AI teams. Let's delve into evaluating AI-driven experiences and the complexities that arise in this dynamic landscape! // Bio Faizaan is an AI Product lead at LinkedIn working on Personalization and Generative AI use cases for Creators and Conversations on LinkedIn. He's been in the field of machine learning for 8+ years now he started as a research assistant eventually transitioning to being a Product Manager during his time at Yahoo. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Faizaan on LinkedIn: https://www.linkedin.com/in/faizaan-charania/ Timestamps: [00:00] Faizaan's preferred beverage [00:22] Takeaways [02:54] The Bollywood actor [05:23] Faizaan's in tech [07:45] Technical pieces to learn about before working at LinkedIn [09:23] Tech Team Data Strategy [12:01] Gradual vs. Advanced ML Implementation [13:36] Shipping on time [14:11] Thoughts on building products with AI [18:20] Push and pull mechanism [21:47] Costs and Choices with AI Models [25:06] AI ROI Evaluation [27:02] Thoughts on open source [28:17] Building Generative AI focus [31:50] Prompts and Anomalies [34:57] Where to have a human in the loop [35:45] Problem-driven AI Tool [37:56] Creator of AI-generated post on LinkedIn [39:50] Product Impact on AI Democratization [41:15] Distinct signals to measure ROI [44:38] PMs learning AI while ML teams learn product [47:22] Gotchas seen when adding a new AI feature [50:00] Evaluation Challenges in Responses [51:55] Who's more confident? [52:55] Wrap up

Nov 03, 202353:14
The Future of Feature Stores and Platforms // Mike Del Balso & Josh Wills // # 186

The Future of Feature Stores and Platforms // Mike Del Balso & Josh Wills // # 186

MLOps podcast #186 with Mike Del Balso, CEO & Co-founder of Tecton and Josh Wills, Angel Investor, The Future of Feature Stores and Platforms. // Abstract Mike and Josh discuss creating templates and working at a detailed level, exploring Tecton's potential for sharing fraud and third-party features. They focus on technical aspects like data handling and optimizing models, emphasizing the significance of quality data for AI systems and the necessity for cohesive feature infrastructure in reaching production stages. // Bio Mike Del Balso Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Josh Wills Josh Wills is an angel investor specializing in data and machine learning infrastructure. He was formerly the head of data engineering at Slack, the director of data science at Cloudera, and a software engineer at Google. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/ Timestamps: [00:00] Introduction to Mike [01:45] Takeaways [03:32] Features of the new paradigm of ML and LLMs [06:00] D. Sculley's papers [13:05] The birth of Feature Store [17:06] Data Pipeline Challenges Addressed [20:00] Operationalizing [26:50] Feature Store Challenges [30:26] Z access [36:23] Addressing Technical Debt Challenges [37:27] Real-Time vs. Batch Processing [47:10] Feature Store Evolution: Apache Iceberg [49:59] Feature Platform: Dedicated Query Engine [54:04] The bottleneck [56:00] LLMs, Feature Stores Overview [1:00:20] Vector databases [1:06:15] Workflow Templating Efficiency [1:08:35] Gamification suggestion for Tecton [1:10:25] Wrap up

Oct 31, 202301:11:14
Lessons on Data Science Leadership // Luigi Patruno // #185

Lessons on Data Science Leadership // Luigi Patruno // #185

MLOps podcast #185 with Luigi Patruno, VP of Data Science at 2U, Inc, Lessons on Data Science Leadership. // AbstractPicture this: you've got data products to manage, and you're in charge of a team. It's not all sunshine and rainbows, right? Luigi dives into the nitty-gritty of the challenges - from juggling data projects to wrangling the team dynamics. It's a real adventure, let me tell you! // Bio Luigi Patruno is a results-driven data science leader passionate about identifying value-add business opportunities and converting these into analytical solutions that deliver measurable business outcomes. As a leader he focuses on defining strategic vision and, through motivation and discipline, driving teams of highly quantitative data scientists, machine learning engineers, and product managers to achieve extraordinary results. He is currently the VP of Data Science at 2U, where he leads the data science department focused on optimizing business operations through advanced analytics, experimentation, and machine learning. He enjoys teaching others how to leverage data science to improve their businesses through public speaking, teaching courses, and writing online at MLinProduction.com. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://mlinproduction.com/ YouTube channel: https://www.youtube.com/playlist?list=PLBLnN4jzkyqkjLIRpDNZcsG7TMMEk9Asa High Output Management book by Andrew Grove: https://www.amazon.nl/-/en/Andrew-S-Grove/dp/0679762884The One Minute Manager by Kenneth Blanchard Ph.D. and Spencer Johnson M.D.: https://www.amazon.com/Minute-Manager-Kenneth-Blanchard-Ph-D/dp/074350917X ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/ Timestamps: [00:00] Luigi's preferred coffee [00:30] Takeaways [03:04] Being practical [05:44] Data-Driven Decision-Making in Management [12:53] Recent Team Win [14:43] The perfect storm [20:22] Change Management and ROI [25:09] Change Management: Navigating Resistance [29:59] Clarifying North Star Communication [36:24] OKRs in Data Science [40:47] Success Likelihood in Business [45:08] Bus problem solution [49:25] Data Science-Platform Collaboration [53:19] Decentralized Platforms Explained [54:38] Data Platform Architecture Overview [57:14] Incentives for Team Motivation [1:09:45] The blind spots [1:12:22] Wrap up

Oct 27, 202301:13:31
Data Platforms in MLOps: Translating Business Goals into Product Decisions // Richa Sachdev // #184

Data Platforms in MLOps: Translating Business Goals into Product Decisions // Richa Sachdev // #184

MLOps podcast #184 with Richa Sachdev, Executive Director- Data Operations and Automation at JP Morgan Chase, Data Platforms in MLOps: Translating Business Goals into Product Decisions. // Abstract Richa, with her background in software engineering and experience in the financial sector, shares her insights on optimizing the end-user experience and the importance of understanding business goals and metrics. She discusses her journey in converting legacy applications, working with data platforms, and the challenges of integrating different databases. Richa also explores the role of automation in streamlining processes and improving customer interactions in the reward space. Join us as we unravel the fascinating world of MLOps and uncover the strategies and technologies that drive success in this ever-evolving field. // Bio A passionate and impact-driven leader whose expertise spans leading teams, architecting ML and data-intensive applications, and driving enterprise data strategy. Richa has worked for a Tier A Start-up developing feature platforms and in financial companies, leading ML Engineering teams to drive data-driven business decisions. Richa enjoys reading technical blogs focussed on system design and plays an active role in the MLOps Community. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.youtube.com/watch?v=i0To3DeHGuU
https://www.youtube.com/watch?v=tAOf2lVQUY4
https://www.youtube.com/watch?v=cXanVyaannQ
https://www.youtube.com/watch?v=2aWSsL24fv8 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Richa on LinkedIn: https://www.linkedin.com/in/richasachdev/ Timestamps: [00:00] Richa's preferred coffee [02:09] Takeaways [04:26] Richa's background to data [08:55] Prescriptive, Descriptive, and Predictive Data [11:50] Data Engineering Perspectives & Setup [17:34] Structured and Unstructured data [21:01] Richa's day-to-day at Chase [23:52] Figure out the business needs before the cool tech [26:46] Importance of business metrics [30:43] Optimizing end-user experience and trade-offs [36:06] Exhausting creativity in finding solutions [37:40] Consider faster implementation and increased ROI [40:20] Banks still using COBOL [41:17] Learning and growing as a versatile leader [42:04] Wrap up

Oct 24, 202342:49
MLOps vs ML Orchestration // Ketan Umare // #183

MLOps vs ML Orchestration // Ketan Umare // #183

MLOps podcast #183 with Ketan Umare, CEO of Union.AI, MLOps vs ML Orchestration co-hosted by Stephen Batifol. // Abstract Let's explore the relationship between Union and Flyte, emphasizing the significance of community-driven development and the challenge of balancing feature requests with security considerations. This conversation highlights the importance of real-time data and secure data handling in orchestrating machine learning models. The Flyte community's empathy and support for newcomers underscore the community's value in democratizing machine learning, making it more accessible and efficient for a broader audience. // Bio Ketan Umare is the CEO and co-founder at Union.ai. Previously he had multiple Senior roles at Lyft, Oracle, and Amazon ranging from Cloud, Distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father, and husband, and enjoys traveling and outdoor activities. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://union.ai/ Flyte: https://flyte.org/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Ketan on LinkedIn: https://www.linkedin.com/in/ketanumare/ Timestamps: [00:00] Ketan's preferred coffee [01:05] Takeaways [03:08] Please like, share, and subscribe to our MLOps channels! [03:15] Shout out to Ketan and UnionAI for sponsoring this episode! [04:23] Orchestration recent changes [07:51] Community with Flyte [11:26] ML orchestration [15:40] 50/50 is generous [20:06] Real-time ML [21:15] Over engineering without benefits [23:20] Balancing everything [27:40] Union verse Flyte [32:52] High value features of Union AI at the back of Flyte [40:18] Building LLM infrastructure [45:30] Traditional ML is the whole prompting [46:46] LLMs to evaluating prompts [48:55] Wrap up

Oct 20, 202349:46
MLOps@GetYourGuide // Jean Machado, Meghana Satish, Olivia Houghton, Theodore Meynard// #182

MLOps@GetYourGuide // Jean Machado, Meghana Satish, Olivia Houghton, Theodore Meynard// #182

MLOps podcast #182 with GetYourGuide's Jean Machado, DataScience Manager, Meghana Satish, MLOps Engineer, Olivia Houghton, Machine Learning Operations Engineer, Theodore Meynard, Data Science Manager, MLOps@GetYourGuide. // Abstract Join a team to talk about the journey of GYG with MLOps. From the conception of their platform to the creation of the MLOps engineer role and to their current stack state. // Bio Jean Machado Jean Carlo Machado is a DataScience Manager at GetYourGuide for the Growth Data Products team and the Machine Learning Platform Team. He is privileged to be able to work on turning ideas in data scinece from inception to production. Before GYG Jean was working in a startup in Brazil building its infrastructure from the ground up. Jean also likes community building and using technology for social good. Meghana Satish Meghana Satish is currently working as an MLOps Engineer at GetYourGuide. She has previously held positions at Amazon AWS in Berlin and Microsoft IT in Hyderabad. In addition to her career in technology, Meghana is also a talented singer, dancer, and yoga practitioner. Olivia Houghton Olivia has been working as an MLOps engineer at GetYourGuide for the past year and a half or so. Olivia's main work is in building and managing their activity ranking service. Theodore Meynard Theodore Meynard, Data Science Manager at GetYourGuide, leads the evolution of their ranking algorithm, enriching customer experiences. His hands-on journey from data scientist to leader has honed his expertise in MLOps and real-time ML. Beyond work, he's a co-organizer of PyData Berlin, underlining his commitment to community and collaborative learning. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://investing1012dot0.substack.com/ The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jean on LinkedIn: https://www.linkedin.com/in/jean-carlo-machado-53b15977/ Connect with Meghana on LinkedIn: https://www.linkedin.com/in/meghana-satish-2a825282/?originalSubdomain=de Connect with Olivia on LinkedIn: https://www.linkedin.com/in/oliviaphoughton/ Connect with Theodore on LinkedIn: https://www.linkedin.com/in/theodore-meynard/

Timestamps: [00:00] GetYourGuide team's preferred coffee [00:55] Takeaways [02:20] Shout out to Berlin MLOps Community [02:38] Please like, share, and subscribe to our MLOps channels! [03:39] The GetYourGuide platform [05:45] GetYourGuide use cases [11:51] Strong Leadership Vision [13:59] Creating rituals [16:55] Feedback on the loop for improvements [18:35] Different components of GetYourGuide's ML Platform [21:04] V2 service templates [24:26] Biggest pain points [27:02] Feature flags [30:51] Data foundation [36:25] Data Testing [39:53] Cross-team Tool Adoption Process [44:59] Regrets about design decisions made in the past [47:53] What's next for the platform with LLMs? [52:49] Non-data scientists suggesting use cases, language flexibility [55:14] DevSecOps team's AI study group ideation [59:25] Experiments in growth data products, marketing split [1:01:47] Shout out to the Berlin MLOps Community! [1:03:31] Wrap up

Oct 20, 202301:03:52
The Centralization of Power in AI // Kyle Harrison // # 181

The Centralization of Power in AI // Kyle Harrison // # 181

MLOps podcast #181 with Kyle Harrison, General Partner at Contrary, The Centralization of Power in AI. // Abstract Kyle Harrison delves into the limitations imposed by language, underscoring how it can impede our grasp and manipulation of reality while stressing the critical need for improved language model performance for real-time applications. He further explores the perils of centralizing power in AI, with a specific focus on the "Openness of AI", where concerns about privacy are brought to the forefront, prompting his call for businesses to reconsider their reliance on it. The discussion also traverses the evolving landscape of AI, drawing comparisons between prominent machine learning frameworks such as TensorFlow and PyTorch. Notably, the episode underscores the vital role of open-source initiatives within the AI community and highlights the unexpected involvement of Meta in driving open-source development. // Bio Kyle Harrison is a General Partner at Contrary, where he leads Series A and growth-stage investing. He joined Contrary from Index where he was a Partner, and before that he was a growth investor at Coatue. His portfolio includes iconic startups and public companies including Ramp, Replit, Cohere, Snowflake, and Databricks. He also regularly shares his analysis on the venture capital landscape via his Substack Investing 101. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://investing1012dot0.substack.com/ The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kyle-harrison-9274b278/ Timestamps: [00:00] Kyle's preferred beverage [00:20] Takeaways [03:52] Hype in technology space [09:20] Application Layer Revenue [14:44] Stability AI Lawsuit [18:08] Concern over concentration of power in AI [20:20] Transparency concerns [23:35] Open Source AI [25:57] To use or not to use Open AI [30:51] Lack of technical expertise and business-building capabilities [35:09] AI Transparency and Accountability [37:50] Traditional ML [41:47] Finding a unique approach [45:41] AGI limitations [47:43] Using Agents [49:46] Agents getting past demos [54:39] Tech Challenges & Hoverboard Dreams [58:04] Both AI hype and skepticism are foolish [01:27] Wrap up

Oct 13, 202301:01:34
Adventures in Building CLIP & Other (Largeish) LMs // Sachin Abeywardana // #180

Adventures in Building CLIP & Other (Largeish) LMs // Sachin Abeywardana // #180

MLOps podcast #180 with Sachin Abeywardana Deep Learning Engineer at Canva AI, Adventures in Building CLIP and Other (Largeish) Language Models sponsored by Prem AI. // Abstract Sachin takes us on an adventure, sharing insights on the pitfalls of not understanding the broader product and the importance of incorporating AI and machine learning capabilities. From the use of AI models to grammar correction and code generation to the fascinating Clip model and the challenges of balancing work and family life, this episode promises to be both informative and thought-provoking. // Bio Sachin is the father of two beautiful children. He completed his PhD in Bayesian Machine Learning at University of Sydney in 2015. In 2016 he discovered Deep Learning and hasn't looked back. He currently works as a Senior Machine Learning Engineer at Canva and is mainly focusing on NLP problems. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sachin Blogs: https://sachinruk.github.io/blog.html https://sachinruk.github.io/blog/ Graph ML link: http://web.stanford.edu/class/cs224w/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sachin on LinkedIn: https://www.linkedin.com/in/sachinabeywardana/ Timestamps: [00:00] Sachin's preferred beverage [00:26] Takeaways [02:30] Chat GPT user [05:58] Understanding on reliable Agents [08:10] Sachin's background [12:45] Staying at Deep Learning [16:17] Recommendation or Lead Scoring [17:36] Vector database [19:00] Sachin's blogs [23:26] The cap people [26:10] Pursuing business case [27:33] Canva [31:16] Incorporating AI and Machine Learning [32:17] Sponsor Ad [38:22] Eliminating unnecessary steps [39:00] Interacting with the product team [43:04] Criticisms on the current architecture limitations [45:58] Insufficient exploration of Transformers [47:42] Explaining GraphML [52:35] Fine-tuning ChatGPT2 [57:54] Leading ML Engineers and teams [59:40] Being practical with Math [1:05:52] Wrap up

Oct 10, 202301:06:38