MLOps.community

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Nikolaus West, CEO of Rerun, breaks down the challenges and opportunities of physical AI—AI that interacts with the real world. He explains why traditional software falls short in dynamic environments and how visualization, adaptability, and better tooling are key to making robotics and spatial computing more practical.

// Bio

Niko is a second-time founder and software engineer with a computer vision background from Stanford. He’s a fanatic about bringing great computer vision and robotics products to the physical world.

// Related Links

Website: rerun.io

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Niko on LinkedIn: /NikolausWest

Timestamps:

[00:00] Niko's preferred coffee

[00:35] Physical AI vs Robotics Debate

[04:40] IoT Hype vs Reality

[12:16] Physical AI Lifecycle Overview

[20:05] AI Constraints in Robotics

[23:42] Data Challenges in Robotics

[33:37] Open Sourcing AI Tools

[39:36] Rerun Platform Integration

[40:57] Data Integration for Insights

[45:02] Data Pipelines and Quality

[49:19] Robotics Design Trade-offs

[52:25] Wrap up

Website: https://www.typedef.ai

https://techontherocks.show

https://www.cpard.xyz

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Kostas on LinkedIn: /kostaspardalis/

Connect with Yoni on LinkedIn: /yonimichael/

Timestamps:

[00:00] Breaking Tools, Evolving Data Workloads

[06:35] Building Truly Great Data Teams

[10:49] Making Data Platforms Actually Useful

[18:54] Scaling AI with Native Integration

[24:04] Empowering Employees to Build Agents

[28:17] Rise of the AI Sherpa

[36:09] Real AI Infrastructure Pain Points

[38:05] Fixing Gaps Between Data, AI

[46:04] Smarter Decisions Through Better Data

[50:18] LLMs as Human-Machine Interfaces

[53:40] Why Summarization Still Falls Short

[01:01:15] Smarter Chunking, Fixing Text Issues

[01:09:08] Evaluating AI with Canary Pipelines

[01:11:46] Finding Use Cases That Matter

[01:17:38] Cutting Costs, Keeping AI Quality

[01:25:15] Aligning MLOps to Business Outcomes

[01:29:44] Communities Thrive on Cross-Pollination

[01:34:56] Evaluation Tools Quietly Consolidating

Website: https://gregkamradt.com/

YouTube channel: https://www.youtube.com/@DataIndependent

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Greg on LinkedIn: /gregkamradt/

Timestamps:

[00:00] Human-Easy, AI-Hard

[05:25] When the Model Shocks Everyone

[06:39] “Let’s Circle Back on That Benchmark…”

[09:50] Want Better AI? Pay the Compute Bill

[14:10] Can We Define Intelligence by How Fast You Learn?

[16:42] Still Waiting on That Algorithmic Breakthrough

[20:00] LangChain Was Just the Beginning

[24:23] Start With Humans, End With AGI

[29:01] What If Reality’s Just... What It Seems?

[32:21] AI Needs Fewer Vibes, More Predictions

[36:02] Defining Intelligence (No Pressure)

[36:41] AI Building AI? Yep, We're Going There

[40:13] Open Source vs. Prize Money Drama

[43:05] Architecting the ARC Challenge

[46:38] Agent 57 and the Atari Gauntlet

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.

// Bio

Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.

As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.

// Related Links

Website: https://www.snowleopard.ai/

AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Deepti on LinkedIn: /thedeepti/

Timestamps:

[00:00] Deepti's preferred coffee

[00:49] MLflow vs Kubeflow Debate

[04:58] GenAI Data Integration Challenges

[09:02] GenAI Sidecar Spicy Takes

[14:07] Troubleshooting LLM Hallucinations

[19:03] AI Overengineering and Hype

[25:06] Self-Serve Analytics Governance

[33:29] Dashboards vs Data Quality

[37:06] Agent Database Context Control

[43:00] LLM as Orchestrator

[47:34] Tool Call Ownership Clarification

[51:45] MCP Server Challenges

[56:52] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs.

// Bio

Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open-source tools. He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem.

// Related Links

Website: https://tiangolo.com/

FastAPI: https://fastapi.tiangolo.com/

FastAPI Cloud: https://fastapicloud.com/

FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Tiangolo on LinkedIn: /tiangolo

Timestamps:

[00:00] Sebastián's preferred coffee

[00:15] Takeaways

[01:43] Why Pydantic is Awesome

[06:47] ML Background and FastAPI

[10:44] NASA FastAPI Emojis

[15:21] FastAPI Cloud Journey

[26:07] FastAPI Cloud Open-Source Balance

[31:45] Basecamp Design Philosophy

[35:30] AI Abstraction Strategies

[42:56] Engineering vs Developer Experience

[51:40] Dogfooding and Docs Strategy

[59:44] Code Simplicity and Trust

[1:04:26] Scaling Without Losing Vision

[1:08:20] FastAPI Cloud Signup

[1:09:23] Wrap up

https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_page

https://cleric.ai/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Shreya on LinkedIn: /shrshnk

Connect with Willem on LinkedIn: /willempienaar

Timestamps:

[00:00] Trust Issues in AI Data

[04:49] Cloud Clarity Meets Retrieval

[09:37] Why Fast AI Is Hard

[11:10] Fixing AI Communication Gaps

[14:53] Smarter Feedback for Prompts

[19:23] Creativity Through Data Exploration

[23:46] Helping Engineers Solve Faster

[26:03] The Three Gaps in AI

[28:08] Alerts Without the Noise

[33:22] Custom vs General AI

[34:14] Sharpening Agent Skills

[40:01] Catching Repeat Failures

[43:38] Rise of Self-Healing Software

[44:12] The Chaos of Monitoring AI

Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.

// Bio

Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.

Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.

// Related Links

Website: https://www.databricks.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Raj on LinkedIn: /rajammanabrolu

Timestamps:

[00:00] Raj's preferred coffee

[00:36] Takeaways

[01:02] Tao Naming Decision

[04:19] No Labels Machine Learning

[08:09] Tao and TAO breakdown

[13:20] Reward Model Fine-Tuning

[18:15] Training vs Inference Compute

[22:32] Retraining and Model Drift

[29:06] Prompt Tuning vs Fine-Tuning

[34:32] Small Model Optimization Strategies

[37:10] Small Model Potential

[43:08] Fine-tuning Model Differences

[46:02] Mistral Model Freedom

[53:46] Wrap up

https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations.

While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.// BioFounder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development.

Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI.

// Related Links

Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform

Jukka's new company: https://8wave.ai

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Jukka on LinkedIn: /jukka-remes

Timestamps:

[00:00] Jukka's preferred coffee

[00:39] Open-Source Platform Benefits

[01:56] Silo MLOps Platform Explanation

[05:18] AI Model Production Processes

[10:42] AI Platform Use Cases

[16:54] Reproducibility in Research Models

[26:51] Pipeline setup automation

[33:26] MLOps Adoption Journey

[38:31] EU AI Act and Open Source

[41:38] MLOps and 8wave AI

[45:46] Optimizing Cross-Stakeholder Collaboration

[52:15] Open Source ML Platform

[55:06] Wrap up

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.

// Bio

Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created 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. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.

// Related Links

Website: ⁠www.tecton.ai⁠

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠

MLOps Swag/Merch: [⁠https://shop.mlops.community/⁠]

Connect with Demetrios on LinkedIn: ⁠/dpbrinkm⁠

Connect with Mike on LinkedIn: ⁠/michaeldelbalso⁠

Timestamps:

[00:00] Smarter decisions, less manual work

[03:52] Data pipelines: pain and fixes

[08:45] Why Tecton was born

[11:30] ML use cases shift

[14:14] Models for big bets

[18:39] Build or buy drama

[20:20] Fintech's data playbook

[23:52] What really needs real-time

[28:07] Speeding up ML delivery

[32:09] Valuing ML is tricky

[35:29] Simplifying ML toolkits

[37:18] AI copilots in action

[42:13] AI that fights fraud

[45:07] Teaming up across coasts

[46:43] Tecton + Generative AI?

Raza Habib, the CEO of LLM Eval platform Humanloop, talks to us about how to make your AI products more accurate and reliable by shortening the feedback loop of your evals. Quickly iterating on prompts and testing what works, along with some of his favorite Dario from Anthropic AI Quotes.

// Bio

Raza is the CEO and Co-founder at Humanloop. He has a PhD in Machine Learning from UCL, was the founding engineer of Monolith AI, and has built speech systems at Google. For the last 4 years, he has led Humanloop and supported leading technology companies such as Duolingo, Vanta, and Gusto to build products with large language models. Raza was featured in the Forbes 30 Under 30 technology list in 2022, and Sifted recently named him one of the most influential Gen AI founders in Europe.

// Related Links

Websites: https://humanloop.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Raza on LinkedIn: /humanloop-raza

Timestamps:

[00:00] Cracking Open System Failures and How We Fix Them

[05:44] LLMs in the Wild — First Steps and Growing Pains

[08:28] Building the Backbone of Tracing and Observability

[13:02] Tuning the Dials for Peak Model Performance

[13:51] From Growing Pains to Glowing Gains in AI Systems

[17:26] Where Prompts Meet Psychology and Code

[22:40] Why Data Experts Deserve a Seat at the Table

[24:59] Humanloop and the Art of Configuration Taming

[28:23] What Actually Matters in Customer-Facing AI

[33:43] Starting Fresh with Private Models That Deliver

[34:58] How LLM Agents Are Changing the Way We Talk

[39:23] The Secret Lives of Prompts Inside Frameworks

[42:58] Streaming Showdowns — Creativity vs. Convenience

[46:26] Meet Our Auto-Tuning AI Prototype

[49:25] Building the Blueprint for Smarter AI

[51:24] Feedback Isn’t Optional — It’s Everything

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here?

// Bio

Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision!

// Related Links

Website: https://www.boundaryml.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Vaibhav on LinkedIn: /vaigup

Timestamps:

[00:00] Vaibhav's preferred coffee

[00:38] What is BAML

[03:07] LangChain Overengineering Issues

[06:46] Verifiable English Explained

[11:45] Python AI Integration Challenges

[15:16] Strings as First-Class Code

[21:45] Platform Gap in Development

[30:06] Workflow Efficiency Tools

[33:10] Surprising BAML Insights

[40:43] BAML Cool Projects

[45:54] BAML Developer Conversations

[48:39] Wrap up

Big thanks to the folks at Rafay for backing this episode — appreciate the support in making these conversations happen!

// Bio

Mohan is a seasoned and innovative product leader currently serving as the Chief Product Officer at Rafay Systems. He has led multi-site teams and driven product strategy at companies like Okta, Neustar, and McAfee.

// Related Links

Websites: https://rafay.co/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Mohan on LinkedIn: /mohanatreya

Timestamps:

[00:00] AI/ML Customer Challenges

[04:21] Dependency on Microsoft for Revenue

[09:08] Challenges of Hypothesis in AI/ML

[12:17] Neo Cloud Onboarding Challenges

[15:02] Elastic GPU Cloud Automation

[19:11] Dynamic GPU Inventory Management

[20:25] Terraform Lacks Inventory Awareness

[26:42] Onboarding and End-User Experience Strategies

[29:30] Optimizing Storage for Data Efficiency

[33:38] Pizza Analogy: User Preferences

[35:18] Token-Based GPU Cloud Monetization

[39:01] Empowering Citizen Scientists with AI

[42:31] Innovative CFO Chatbot Solutions

[47:09] Cloud Services Need Spectrum

Websites: arcade.dev // aihero.studio

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Rahul on LinkedIn: /rparundekar

Connect with Sam on LinkedIn: /sampartee

Timestamps:

[00:00] Agents & Tools, Explained (Without Melting Your Brain)

[09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It)

[13:18] Can We Actually Trust the Protocol?

[18:13] KYC, But Make It AI (and Less Painful)

[25:25] Web Automation Tests: The Bugs Strike Back

[28:18] MCP Dev: What Went Wrong (and What Saved Us)

[33:53] Social Login: One Button to Rule Them All

[39:33] What Even Is an AI-Native Developer?

[42:21] Betting Big on Smarter Models (High Risk, High Reward)

[51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks)

[55:31] Async Task Handoffs: Herding Cats, But Digitally

[1:00:37] Getting AI to Actually Help Your Workflow

[1:03:53] The Infamous Varma System Error (And How We Dodge It)

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space.

// Bio

Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads.

Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices.

Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value.

// Related Links

Website: https://dealroom.net

https://www.mascience.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Kison on LinkedIn: /kisonpatel

Timestamps:

[00:00] Kison's preferred coffee

[00:11] Takeaways

[00:40] Founders Journey Slumps

[05:07] Jira for M&A

[10:57] Overcoming Idea Paralysis

[14:32] Customer-led Discovery Success

[22:20] Legal Fees in Deals

[26:24] Data Room Differentiators

[29:26] PLG vs Sales Teams

[31:43] AI Pricing Strategies

[35:15] PLG AI Cost Optimization

[40:53] Building AI Teams

[47:40] Great Companies Are Bought

[51:10] M&A Failures and Fever

[54:23] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.

// Bio

Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck.

His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.

Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.

// Related Links

Website: aibuilders.club

Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Fausto on LinkedIn: /stepintoliquid

Timestamps:

[00:00] Fausto's preferred coffee

[00:26] Takeaways

[01:18] Automated Ad Creative Generation

[07:14] AI in Marketing Workflows

[13:23] MCP and System Bottlenecks

[21:45] Forward Compatibility vs Optimization

[29:57] Unlocking Workflow Speed

[33:48] AI Dependency vs Critical Thinking

[37:44] AI Realism and Paradoxes

[42:30] Outsourcing Decision-Making Risks

[46:22] Human Value in Automation

[49:02] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features.

// Bio

Maria is an MLOps Tech lead with over 10 years of experience in Data and AI.

For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations.

Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs.

// Related Links

Website: marvelousmlops.io

MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Maria on LinkedIn: /maria-vechtomova

Timestamps:

[00:00] Maria's preferred coffee

[00:42] Takeaways

[02:48] Why Databricks for MLOps

[09:56] Platform Adoption vs Procurement Pain

[12:56] Databricks Best Practices

[16:57] Feature Store Overview

[22:00] Managed system trade-offs

[29:15] Databricks Developments and Trends

[44:31] Insider Info and Summit

[45:47] Data Ownership Pros and Cons

[48:08] Data Contracts and Challenges

[51:25] MLOps Databricks Book Guide

[52:19] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

Huge shout-out to RagMetrics for sponsoring this episode!

// Abstract

Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators.

// Bio

Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager.Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player.

// Related Links

Website: ragmetrics.ai

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alon on LinkedIn: /alonbochman

Timestamps:

[00:00] Alon's preferred coffee

[00:15] Takeaways

[00:47] Testing Multi-Agent Systems

[05:55] Tracking ML Experiments

[12:28] AI Eval Redundancy Balance

[17:07] Handcrafted vs LLM Eval Tradeoffs

[28:15] LLM Judging Mechanisms

[36:03] AI and Human Judgment

[38:55] Document Evaluation with LLM

[42:08] Subject Matter Expertise in Co-Pilots

[46:33] LLMs as Judges

[51:40] LLM Evaluation Best Practices

[55:26] LM Judge Evaluation Criteria

[58:15] Visualizing AI Outputs

[1:01:16] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Open-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure.

// Bio

The best meme-maker in Tech. Writer on AI, Software, and the Tech Industry.

// Related Links

Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/

https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Devansh on LinkedIn: /devansh-devansh-516004168

Timestamps:

[00:00] Devansh's preferred coffee

[01:23] Jailbreaking DeepSeek

[02:24] AI Made Simple

[07:16] Leveraging AI for Data Insights

[10:42] Synthetic Data and LLMs

[19:29] AI Experience Design

[22:20] Synthetic Data Bias Reduction

[26:33] Data Ecosystem Insights

[29:50] Moving Intelligence to Data Layer

[36:37] Minimizing Model Responsibility

[40:04] Workflow vs Generalized Agents

[49:24] AI Second-Order Effects

[55:26] AI Experience vs Efficiency

[1:01:10] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Existing BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data.

Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability.

We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data.

// Bio

Paco Nathan

Paco is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is lead committer for the pytextrank​ and kglab​ open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks.

Weidong Yang

Weidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics based solutions to address complex big data problems. His expertise spans Physics, Computer Science and Performing Art, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production.

// Related Links

Website: https://www.kineviz.com/

Blog: https://medium.com/kineviz

Website: https://derwen.ai/pacohttps://huggingface.co/pacoid

https://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Weidong on LinkedIn: /yangweidong/

Connect with Paco on LinkedIn: /ceteri/

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

A discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data.

// Bio

Second Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers

// Related Links

Website: tryardent.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Vikram on LinkedIn: /vikram-chennai/

Timestamps:

[00:00] Vikram's preferred coffee

[00:09] Takeaways

[00:42] Please like, share, leave a review, and subscribe to our MLOps channels! You can give us up to 5 stars on Spotify and leave your reviews!

[01:53] Product User Categories

[02:47] AI Data Engineer Role

[05:40] AI Coding Limits Enterprise

[09:22] Creating Feedback Loops

[14:23] Breaking Down Big Tasks

[19:39] Marketing Data Agent Scope

[28:03] Clear Success Metrics

[32:20] Creating Agent Glossary

[36:43] AI Prompt Toolkits

[38:54] Pricing Strategy Discussion

[43:20] Compute Abstraction and Pipelines

[45:23] Agent Surprises and Logs

[47:12] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

What does it mean to MLOps now? Everyone is trying to make a killing from AI, everyone wants the freshest technology to show off as part of their product. But what impact does that have on the "journey of the model". Do we still think about how an idea makes it's way to production to make money? How can we get better at it, maybe the answer lies in the ancient "non-AI" past...

// Bio

For the majority of my career I have been a "full stack" developer with a leaning towards devops and platforms. In the last four years or so, I have worked on ML Platforms. I find that applying good software engineering practises is more important than ever in this AI fueled world.

// Related Links

Blogs: https://medium.com/@sashman90/mlops-the-evolution-of-the-t-shaped-engineer-a4d8a24a4042

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Sash on LinkedIn: /oleksandr-stasyk-5751946b

Timestamps:

[00:00] Sash's preferred coffee

[00:09] Takeaways

[01:21] Vibe Coding Reality Check

[06:27] MLOps and Vibe Coding

[12:53] Data Engineering in GenAI

[14:53] MLOps in MVP Development

[21:13] Platform Engineering Org Models

[27:30] Empathy in Data Engineering

[31:11] Post-DevOps MLOps Evolution

[39:32] AI for Fast Feedback

[46:53] AI Workflow vs Real Work

[50:13] ML Confession Stories

[59:06] Shift Left in Testing

[1:05:49] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Between Uber’s partnership with NVIDIA and speculation around the U.S.'s President Donald Trump enacting policies that allow fully autonomous vehicles, it’s more important than ever to ensure the accuracy of machine learning models. Yet, the public’s confidence in AVs is shaky due to scary accidents caused by gaps in the tech that Sama is looking to fill.As one of the industry’s top leaders, Duncan Curtis, SVP of Product and Technology at Sama, would be delighted to share how we can improve the accuracy, speed, and cost-efficiency of ML algorithms for ​A​Vs. Sama’s machine learning technologies minimize the risk of model failure and lower the total cost of ownership for car manufacturers including Ford, BMW, and GM, as well as four of the five top OEMs and their Tier 1 suppliers. This is especially timely as Tesla is under investigation for crashes due to its Smart Summon feature and Waymo recently had a passenger trapped in one of its driverless taxis.

// Bio

Duncan Curtis is the SVP of Product at Sama, a leader in de-risking ML models, delivering best-in-class data annotation solutions with our enterprise-strength, experience & expertise, and ethical AI approach. To this leadership role, he brings 4 years of Autonomous Vehicle experience as the Head of Product at Zoox (now part of Amazon) and VP of Product at Aptiv, and 4 years of AI experience as a product manager at Google where he delighted the +1B daily active users of the Play Store and Play Games.

// Related Links

Website: https://www.sama.com/

Tesla is under investigation: https://www.cnn.com/2025/01/07/business/nhtsa-tesla-smart-summon-probe/index.html

Waymo recently had a passenger trapped: https://www.cbsnews.com/losangeles/news/la-man-nearly-misses-flight-as-self-driving-waymo-taxi-drives-around-parking-lot-in-circles/

https://coruzant.com/profiles/duncan-curtis/

https://builtin.com/articles/remove-bias-from-machine-learning-algorithms

Look At Your ****ing Data :eyes: // Kenny Daniel // MLOps Podcast #292: https://youtu.be/6EMnkAHmoag

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Luca on LinkedIn: /duncan-curtis

Timestamps:

[00:00] Duncan's preferred coffee

[00:08] Takeaways

[01:00] AI Enterprise Focus

[04:18] Human-in-the-loop Efficiency

[08:42] Edge Cases in AI

[14:14] Forward Combat Compatibility Failures

[17:30] Specialized Data Annotation Challenges

[24:44] SAM for Ring Integration

[28:50] Data Bottleneck in AI

[31:29] Data Connector Horror Story

[33:17] Sama AI Data Annotation

[37:20] Cool Business Problems Solved

[40:50] AI ROI Framework

[45:11] Wrap up

Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Alexa shares her journey into software engineering, from early struggles with Airflow and Kubernetes to leading open-source projects like the Envoy AI Gateway. She and Demetrios discuss AI model deployment, tooling differences across tech roles, and the importance of abstraction. They highlight aligning technical work with business goals and improving cross-team communication, offering key insights into MLOps and AI infrastructure.

// Bio

Alexa Griffith is a Senior Software Engineer at Bloomberg, where she builds scalable inference platforms for machine learning workflows and contributes to open-source projects like KServe. She began her career at Bluecore working in data science infrastructure, and holds an honors degree in Chemistry from the University of Tennessee, Knoxville. She shares her insights through her podcast, Alexa’s Input (AI), technical blogs, and active engagement with the tech community at conferences and meetups.

// Related LinksWebsite: https://alexagriffith.com/

Kubecon Keynote about Envoy AI Gateway https://www.youtube.com/watch?v=do1viOk8nok

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity][https://x.com/mlopscommunity] or LinkedIn [https://go.mlops.community/linkedin]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alexa on LinkedIn: /alexa-griffith

Future of Software, Agents in the Enterprise, and Inception Stage Company Building // MLOps Podcast 293 with Eliot Durbin, General Partner at Boldstart Ventures.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter

// AbstractKey lessons for founders that are thinking about or starting their companies. 15 years of inception stage investing from how data science companies like Yhat went to market in 2013-14 and how that's evolved, to building companies around OSS frameworks like CrewAI; Eliot share's key learnings and questions for founders starting out.

// BioEliot is a General Partner @ boldstart ventures since it's founding in 2010. boldstart an inception stage lead investor for technical founders building the next generation of enterprise companies such as Clay, Snyk, BigID, Kustomer, Superhuman, and CrewAI.

// Related LinksWebsite: boldstart.vchttps://medium.com/@etdurbin

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or LinkedIn (https://go.mlops.community/linkedin) Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Eliot on LinkedIn: /eliotdurbin

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity][https://x.com/mlopscommunity] or LinkedIn [https://go.mlops.community/linkedin]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity][https://x.com/mlopscommunity] or LinkedIn [https://go.mlops.community/linkedin]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity][https://x.com/mlopscommunity] or LinkedIn [https://go.mlops.community/linkedin]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

In Agents in Production Series - Episode One, Demetrios chats with Paul van der Boor and Floris Fok about the real-world challenges of deploying AI agents across  @ProsusGroup  of companies. They break down the evolution from simple LLMs to fully interactive systems, tackling scale, UX, and the harsh lessons from failed projects. Packed with insights on what works (and what doesn’t), this episode is a must-listen for anyone serious about AI in production.

Guest speakers: Paul van der Boor - VP AI at Prosus Group

Floris Fok - AI Engineer at Prosus Group

Host:Demetrios Brinkmann - Founder of MLOps Community

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠

Join our slack community [⁠https://go.mlops.community/slack⁠]

Follow us on X/Twitter [⁠@mlopscommunity⁠][⁠https://x.com/mlopscommunity⁠] or LinkedIn [⁠https://go.mlops.community/linkedin⁠]

Sign up for the next meetup: [⁠https://go.mlops.community/register⁠]

MLOps Swag/Merch: [⁠https://shop.mlops.community/⁠]

Connect with Demetrios on LinkedIn: ⁠/dpbrinkm

Alex Milowski is a researcher, developer, entrepreneur, mathematician, and computer scientist.Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist.// AbstractThere seems to be a shift from workflow languages to code - mostly annotation pythons - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs and at MicroByre, we used a external workflow langauge. Both had a batch task executor on K8s but at MicroByre, we had human and robot in the loop workflows.// BioDr. Milowski is a serial entrepreneur and computer scientist with experience in a variety of data and machine learning technologies. He holds a PhD in Informatics (Computer Science) from the University of Edinburgh, where he researched large-scale computation over scientific data. Over the years, he's spent many years working on various aspects of workflow orchestration in industry, standardization, and in research.// MLOps Swag/Merchhttps://shop.mlops.community/// Related LinksWebsite: https://www.milowski.com/ --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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/alexmilowski/

Willem Pienaar is the Co-Founder and CTO ofCleric. He previously worked at Tecton as a Principal Engineer. Willem Pienaar attended the Georgia Institute of Technology.

Insights from Cleric: Building an Autonomous AI SRE // MLOps Podcast #289 with Willem Pienaar, CTO & Co-Founder of Cleric.// AbstractIn this MLOps Community Podcast episode, Willem Pienaar, CTO of Cleric, breaks down how they built an autonomous AI SRE that helps engineering teams diagnose production issues. We explore how Cleric builds knowledge graphs for system understanding, and uses existing tools/systems during investigations. We also get into some gnarly challenges around memory, tool integration, and evaluation frameworks, and some lessons learned from deploying to engineering teams.// BioWillem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories.Before starting Cleric, Willem led the open-source engineering team at Tecton and established the ML platform team at Gojek, where he built high-scale ML systems for the Southeast Asian Decacorn.// MLOps Swag/Merchhttps://shop.mlops.community/// Related LinksWebsite: willem.co --------------- ✌️Connect With Us ✌️ -------------Join our slack community:https://go.mlops.community/slackFollow us on Twitter:@mlopscommunitySign up for the next meetup:https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more:https://mlops.community/Connect with Demetrios on LinkedIn:https://www.linkedin.com/in/dpbrinkm/Connect with Willem on LinkedIn:https://www.linkedin.com/in/willempienaar/

Vinu Sankar Sadasivan is a CS PhD ... Currently, I am working as a full-time Student Researcher at Google DeepMind on jailbreaking multimodal AI models. Robustness, Detectability, and Data Privacy in AI // MLOps Podcast #289 with Vinu Sankar Sadasivan, Student Researcher at Google DeepMind. // Abstract Recent rapid advancements in Artificial Intelligence (AI) have made it widely applicable across various domains, from autonomous systems to multimodal content generation. However, these models remain susceptible to significant security and safety vulnerabilities. Such weaknesses can enable attackers to jailbreak systems, allowing them to perform harmful tasks or leak sensitive information. As AI becomes increasingly integrated into critical applications like autonomous robotics and healthcare, the importance of ensuring AI safety is growing. Understanding the vulnerabilities in today’s AI systems is crucial to addressing these concerns. // Bio Vinu Sankar Sadasivan is a final-year Computer Science PhD candidate at The University of Maryland, College Park, advised by Prof. Soheil Feizi. His research focuses on Security and Privacy in AI, with a particular emphasis on AI robustness, detectability, and user privacy. Currently, Vinu is a full-time Student Researcher at Google DeepMind, working on jailbreaking multimodal AI models. Previously, Vinu was a Research Scientist intern at Meta FAIR in Paris, where he worked on AI watermarking. Vinu is a recipient of the 2023 Kulkarni Fellowship and has earned several distinctions, including the prestigious Director’s Silver Medal. He completed a Bachelor’s degree in Computer Science & Engineering at IIT Gandhinagar in 2020. Prior to their PhD, Vinu gained research experience as a Junior Research Fellow in the Data Science Lab at IIT Gandhinagar and through internships at Caltech, Microsoft Research India, and IISc. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://vinusankars.github.io/ --------------- ✌️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 Richard on LinkedIn: https://www.linkedin.com/in/vinusankars/

Richard Cloete is a computer scientist and a Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. He is a member of the Galileo Project working under the supervision of Professor Avi, having recently held a postdoctoral position at the University of Cambridge, UK. AI & Aliens: New Eyes on Ancient Questions // MLOps Podcast #288 with Richard Cloete, Laukien-Oumuamua Postdoctoral Research Fellow at Harvard University. // Abstract Demetrios speaks with Dr. Richard Cloete, a Harvard computer scientist and founder of SEAQR Robotics, about his AI-driven work in tracking Unidentified Aerial Phenomena (UAPs) through the Galileo Project. Dr. Cloete explains their advanced sensor setup and the challenges of training AI in this niche field, leading to the creation of AeroSynth, a synthetic data tool. He also discusses his collaboration with the Minor Planet Center on using AI to classify interstellar objects and upcoming telescope data. Additionally, he introduces Seeker Robotics, applying similar AI techniques to oceanic research with unmanned vehicles for marine monitoring. The conversation explores AI’s role in advancing our understanding of space and the ocean. // Bio Richard is a computer scientist and Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. As a member of the Galileo Project under Professor Avi Loeb's supervision, he develops AI models for detecting and tracking aerial objects, specializing in Unidentified Anomalous Phenomena (UAP). Beyond UAP research, he collaborates with astronomers at the Minor Planet Center to create AI models for identifying potential interstellar objects using the upcoming Vera C. Rubin Observatory. Richard is also the CEO and co-founder of SEAQR Robotics, a startup developing advanced unmanned surface vehicles to accelerate the discovery of novel life and phenomena in Earth's oceans and atmosphere. Before joining Harvard, he completed a postdoctoral fellowship at the University of Cambridge, UK, where his research explored the intersection of emerging technologies and law.Grew up in Cape Town, South Africa, where I used to build Tesla Coils, plasma globes, radio stethoscopes, microwave guns, AM radios, and bombs... // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: www.seaqr.net https://itc.cfa.harvard.edu/people/richard-cloete --------------- ✌️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 Richard on LinkedIn: https://www.linkedin.com/in/richard-cloete/

A software engineer based in Delft, Alex Strick van Linschoten recently built Ekko, an open-source framework for adding real-time infrastructure and in-transit message processing to web applications. With years of experience in Ruby, JavaScript, Go, PostgreSQL, AWS, and Docker, I bring a versatile skill set to the table. I hold a PhD in History, have authored books on Afghanistan, and currently work as an ML Engineer at ZenML. Real LLM Success Stories: How They Actually Work // MLOps Podcast #287 with Alex Strick van Linschoten, ML Engineer at ZenML. // Abstract Alex Strick van Linschoten, a machine learning engineer at ZenML, joins the MLOps Community podcast to discuss his comprehensive database of real-world LLM use cases. Drawing inspiration from Evidently AI, Alex created the database to organize fragmented information on LLM usage, covering everything from common chatbot implementations to innovative applications across sectors. They discuss the technical challenges and successes in deploying LLMs, emphasizing the importance of foundational MLOps practices. The episode concludes with a call for community contributions to further enrich the database and collective knowledge of LLM applications. // Bio Alex is a Software Engineer based in the Netherlands, working as a Machine Learning Engineer at ZenML. He previously was awarded a PhD in History (specialism: War Studies) from King's College London and has authored several critically acclaimed books based on his research work in Afghanistan. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://mlops.systemshttps://www.zenml.io/llmops-databasehttps://www.zenml.io/llmops-databasehttps://www.zenml.io/blog/llmops-in-production-457-case-studies-of-what-actually-workshttps://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llmshttps://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementationshttps://huggingface.co/datasets/zenml/llmops-database --------------- ✌️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/strickvl

In his 13 years of software engineering, Ilya Reznik has specialized in commercializing machine learning solutions and building robust ML platforms. He's held technical lead and staff engineering roles at premier firms like Adobe, Twitter, and Meta. Currently, Ilya channels his expertise into his travel startup, Jaunt, while consulting and advising emerging startups. Navigating Machine Learning Careers: Insights from Meta to Consulting // MLOps Podcast #286 with Ilya Reznik, ML Engineering Thought Leader at Instructed Machines, LLC. // Abstract Ilya Reznik's insights into machine learning and career development within the field. With over 13 years of experience at leading tech companies such as Meta, Adobe, and Twitter, Ilya emphasizes the limitations of traditional model fine-tuning methods. He advocates for alternatives like prompt engineering and knowledge retrieval, highlighting their potential to enhance AI performance without the drawbacks associated with fine-tuning. Ilya's recent discussions at the NeurIPS conference reflect a shift towards practical applications of Transformer models and innovative strategies like curriculum learning. Additionally, he shares valuable perspectives on navigating career progression in tech, offering guidance for aspiring ML engineers aiming for senior roles. His narrative serves as a blend of technical expertise and practical career advice, making it a significant resource for professionals in the AI domain. // Bio Ilya has navigated a diverse career path since 2011, transitioning from physicist to software engineer, data scientist, ML engineer, and now content creator. He is passionate about helping ML engineers advance their careers and making AI more impactful and beneficial for society. Previously, Ilya was a technical lead at Meta, where he contributed to 12% of the company’s revenue and managed approximately 30 production ML models. He also worked at Twitter, overseeing offline model evaluation, and at Adobe, where his team was responsible for all intelligent services within Adobe Analytics. Based in Salt Lake City, Ilya enjoys the outdoors, tinkering with Arduino electronics, and, most importantly, spending time with his family. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: mlepath.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 Ilya on LinkedIn: https://www.linkedin.com/in/ibreznik/

Tomaž Levak is the Co-founder and CEO of Trace Labs – OriginTrail core developers. OriginTrail is a web3 infrastructure project combining a decentralized knowledge graph (DKG) and blockchain technologies to create a neutral, inclusive ecosystem. Collective Memory for AI on Decentralized Knowledge Graph // MLOps Podcast #285 with Tomaz Levak, Founder of Trace Labs, Core Developers of OriginTrail. // Abstract The talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail’s symbolic AI fundamentals (i.e. knowledge graphs) and go over details how decentralization improves data integrity, provenance, and user control. We’ll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We’ll explore practical use cases from the enterprise sector as well as latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS and the general convergence of AI, Internet and Crypto. // Bio Tomaz Levak, founder of OriginTrail, is active at the intersection of Cryptocurrency, the Internet, and Artificial Intelligence (AI). At the core of OriginTrail is a pursuit of Verifiable Internet for AI, an inclusive framework addressing critical challenges of the world in an AI era. To achieve the goal of Verifiable Internet for AI, OriginTrail's trusted knowledge foundation ensures the provenance and verifiability of information while incentivizing the creation of high-quality knowledge. These advancements are pivotal to unlock the full potential of AI as they minimize the technology’s shortfalls such as hallucinations, bias, issues of data ownership, and model collapse. Tomaz's contributions to OriginTrail span over a decade and across multiple fields. He is involved in strategic technical innovations for OriginTrail Decentralized Knowledge Graph (DKG) and NeuroWeb blockchain and was among the authors of all three foundational White Paper documents that defined how OriginTrail technology addresses global challenges. Tomaz contributed to the design of OriginTrail token economies and is driving adoption with global brands such as British Standards Institution, Swiss Federal Railways and World Federation of Haemophilia, among others. Committed to the ongoing expansion of the OriginTrail ecosystem, Tomaz is a regular speaker at key industry events. In his appearances, he highlights the significant value that the OriginTrail DKG brings to diverse sectors, including supply chains, life sciences, healthcare, and scientific research. In a rapidly evolving digital landscape, Tomaz and the OriginTrail ecosystem as a whole are playing an important role in ensuring a more inclusive, transparent and decentralized AI. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://origintrail.io

Song recommendation: https://open.spotify.com/track/5GGHmGNZYnVSdRERLUSB4w?si=ae744c3ad528424b --------------- ✌️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 Tomaz on LinkedIn: https://www.linkedin.com/in/tomazlevak/

Krishna Sridhar is an experienced engineering leader passionate about building wonderful products powered by machine learning. Efficient Deployment of Models at the Edge // MLOps Podcast #284 with Krishna Sridhar, Vice President of Qualcomm.

Big shout out to Qualcomm for sponsoring this episode! // Abstract Qualcomm® AI Hub helps to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases. With Qualcomm® AI Hub, you can: Convert trained models from frameworks like PyTorch and ONNX for optimized on-device performance on Qualcomm® devices. Profile models on-device to obtain detailed metrics including runtime, load time, and compute unit utilization. Verify numerical correctness by performing on-device inference. Easily deploy models using Qualcomm® AI Engine Direct, TensorFlow Lite, or ONNX Runtime. The Qualcomm® AI Hub Models repository contains a collection of example models that use Qualcomm® AI Hub to optimize, validate, and deploy models on Qualcomm® devices. Qualcomm® AI Hub automatically handles model translation from source framework to device runtime, applying hardware-aware optimizations, and performs physical performance/numerical validation. The system automatically provisions devices in the cloud for on-device profiling and inference. The following image shows the steps taken to analyze a model using Qualcomm® AI Hub. // Bio Krishna Sridhar leads engineering for Qualcomm™ AI Hub, a system used by more than 10,000 AI developers spanning 1,000 companies to run more than 100,000 models on Qualcomm platforms. Prior to joining Qualcomm, he was Co-founder and CEO of Tetra AI which made its easy to efficiently deploy ML models on mobile/edge hardware. Prior to Tetra AI, Krishna helped design Apple's CoreML which was a software system mission critical to running several experiences at Apple including Camera, Photos, Siri, FaceTime, Watch, and many more across all major Apple device operating systems and all hardware and IP blocks. He has a Ph.D. in computer science from the University of Wisconsin-Madison, and a bachelor’s degree in computer science from Birla Institute of Technology and Science, Pilani, India. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://www.linkedin.com/in/srikris/ --------------- ✌️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 Krishna on LinkedIn: https://www.linkedin.com/in/srikris/