MLOps.community

Jens Bodal is a Senior Software Engineer II working independently, focusing on backend systems, software architecture, and building scalable solutions across client projects.This One Shift Makes Developers Obsolete // MLOps Podcast #366 with Jens Bodal, Senior Software Engineer II, Independent Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract AI agents are shifting the role of developers from writing code to defining intent. This conversation explores why specs are becoming more important than implementation, what breaks in real-world systems, and how engineering teams need to rethink workflows in an agent-driven world.// BioJens Bodal is a senior software engineer based in Edmonds, Washington, with nine years of experience building developer tooling, internal platforms, and web infrastructure. He spent seven years as an SDE II at Amazon, working on teams including Amazon Games Studio and the AWS Events Management Platform. His work has focused on developer tooling, CI/CD systems, testing infrastructure, and improving the developer experience for teams operating production services. He is particularly interested in developer experience and the growing ecosystem of local tools that help engineers build and run AI systems on infrastructure they control.// Related LinksWebsite: https://bodal.devhttps://github.com/jensbodalhttps://www.youtube.com/watch?v=Yp7LYdbOuwE~~~~~~~~ ✌️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: /dpbrinkmConnect with Jens on LinkedIn: /jensbodal

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Big shout-out to  Databricks for the collaboration!

// Abstract

This panel discusses the real-world challenges of deploying AI agents at scale. The conversation explores technical and operational barriers that slow production adoption, including reliability, cost, governance, and security.

The panelists also examine how LLMOps, AIOps, and AgentOps differ from traditional MLOps, and why new approaches are required for generative and agent-based systems. Finally, experts define success criteria for GenAI frameworks, with a focus on robust evaluation, observability, and continuous monitoring across development and staging environments.

// Bio

Samraj Moorjani

Samraj is a software engineer working on the Agent Quality team. Previously, Samraj worked at Meta on ads/product classification research and AppLovin on MLOps. Samraj graduated with a BS+MS in Computer Science from UIUC, advised by Professor Hari Sundaram, where he worked on controllable natural language generation to produce appealing, interpretable science to combat the spread of misinformation. He also worked with Professor Wen-mei Hwu on accelerating LLM inference through extreme sparsification.

Apurva Misra

Apurva is an AI Consultant at Sentick, focusing on assisting startups with their AI strategy and building solutions. She leverages her extensive experience in machine learning and a Master's degree from the University of Waterloo, where her research bridged driving and machine learning, to offer valuable insights. Apurva's keen interest in the startup world fuels her passion for helping emerging companies incorporate AI effectively. In her free time, she is learning Spanish, and she also enjoys exploring hidden gem eateries, always eager to hear about new favourite spots!

Ben Epstein

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now the Co-founder and CTO at GrottoAI, focused on supercharging multifamily teams and reducing vacancy loss with AI-powered guidance for leasing and renewals. Ben also works as an adjunct professor at Washington University in St. Louis, teaching concepts in cloud computing and big data analytics.

Hosted by Adam Becker

// Related Links

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

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// Abstract

Meet arrowspace — an open-source library for curating and understanding LLM datasets across the entire lifecycle, from pre-training to inference. Instead of treating embeddings as static vectors, arrowspace turns them into graphs (“graph wiring”) so you can explore structure, not just similarity. That unlocks smarter RAG search (beyond basic semantic matching), dataset fingerprinting, and deeper insights into how different datasets behave.

You can compare datasets, predict how changes will affect performance, detect drift early, and even safely mix data sources while measuring outcomes.

In short: arrowspace helps you see your data — and make better decisions because of it.

// Bio

With over a decade of experience in software and data engineering across startups and early-stage projects, Lorenzo has recently turned his focus to the AI-assisted movement to automate software and data operations. He has contributed to and founded projects within various open-source communities, including work with Summer of Code, where he focused on the Semantic Web and REST APIs.A strong enthusiast of Python and Rust, he develops tools centered around LLMs and agentic systems. He is a maintainer of the SmartCore ML library, as well as the creator of Arrowspace and the Topological Transformer.

// Related Links

Website: https://www.tuned.org.uk

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Timestamps:

[00:00] Graph Wiring for ML

[00:32] RAG and Vector Similarity

[08:58] Geometric Search Trade-offs

[13:12] Vector DB Algorithm Integration

[21:32] Feature-Based Retrieval Shift

[26:04] Epiplexity and Embeddings

[31:26] Epiplexity and Embedding Structure

[40:15] Training vs Post-hoc Models

[47:16] Discovery-Driven Development

[51:22] Updating Mental Models

[53:00] Vector Search vs Agents

[55:30] Wrap up

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// Abstract

Marketplaces are about to get weird.

With Pedro Chaves and Donné Stevenson: agents picking your house, negotiating deals, even talking to other agents for you.

Less browsing. Less choice. More automation.

Convenience… or giving up control?

// Bio

Donné Stevenson

Focused on building AI-powered products that give companies the tools and expertise needed to harness the power of AI in their respective fields.

Pedro Chaves

Pedro is a Data Science Manager at OLX Group, where he leads teams building machine learning solutions to improve marketplace performance, pricing, and user experience at scale.

// Related Links

Website: https://www.prosus.com/

Website: https://www.olxgroup.com/

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Timestamps:

[00:00] OLX: Disrupting Buyer-Seller Experiences

[03:33] Redefining the Home-Buying Experience

[07:40] User Feedback and Iterative Rollouts

[11:25] Beyond Chat: Redefining Agent Use

[14:03] User Trust and Education Challenges

[16:47] Learning Curve for Automoto

[20:05] Interactive Decision-Making with AI

[24:47] Agents Simplify Buyer-Seller Search

[28:14] Garage Sale Treasure Hunting

[33:43] Agent Discovery Layer Needed

[34:53] Agents Relying on Agents

[39:48] Reducing Friction in Selling Stuff

[41:39] Extracting Buyer Intent Systematically

[44:49] Optimizing Delivery with Lockers

[50:10] Generative AI Commerce Strategies

[51:03] Improving Chat Interaction Layer

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Big shoutout to ⁨ @Temporalio  for the support, and to  @trychroma  for hosting us in their recording studio

// Abstract

A new paradigm is emerging for building applications that process large volumes of data, run for long periods of time, and interact with their environment. It’s called Durable Execution and is replacing traditional data pipelines with a more flexible approach. Durable Execution makes regular code reliable and scalable.

In the past, reliability and scalability have come from restricted programming models, like SQL or MapReduce, but with Durable Execution, this is no longer the case. We can now see data pipelines that include document processing workflows, deep research with LLMs, and other complex and LLM-driven agentic patterns expressed at scale with regular Python programs.

In this session, we describe Durable Execution and explain how it fits in with agents and LLMs to enable a new class of machine learning applications.

// Related Links

https://t.mp/hello?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johann

https://t.mp/vibe?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johann

https://t.mp/career?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johann

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https://luma.com/codingagents

Chris Fregly is currently focused on building and scaling high-performance AI systems, writing and teaching about AI infrastructure, helping organizations adopt generative AI and performance engineering principles on AWS, and fostering large developer communities around these topics.

Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs // MLOps Podcast #363 with Chris Fregly, Founder, AI Performance Engineer, and Investor

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// Abstract

In today’s era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering).

This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference.

// Bio

Chris Fregly is an AI performance engineer and startup founder with experience at AWS, Databricks, and Netflix. He's the author of three (3) O'Reilly books, including Data Science on AWS (2021), Generative AI on AWS (2023), and AI Systems Performance Engineering (2025). He also runs the global AI Performance Engineering meetup and speaks at many AI-related conferences, including Nvidia GTC, ODSC, Big Data London, and more.

// Related Links

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 1st Edition by Chris Fregly: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/

Coding Agents Conference: https://luma.com/codingagents

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Timestamps:

[00:00] SageMaker HyperPod Resilience

[00:27] Book Creation and Software Engineering

[04:57] Software Engineers and Maintenance

[11:49] AI Systems Performance Engineering

[22:03] Cognitive Biases and Optimization / "Mechanical Sympathy"

[29:36] GPU Rack-Scale Architecture

[33:58] Data Center Reliability Issues

[43:52] AI Compute Platforms

[49:05] Hardware vs Ecosystem Choice

[1:00:05] Claude vs Codex vs Gemini

[1:14:53] Kernel Budget Allocation

[1:18:49] Steerable Reasoning Challenges

[1:24:18] Data Chain Value Awareness

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// Abstract

Experimenting with LLMs is easy. Running them reliably and cost-effectively in production is where things break.

Most AI teams never make it past demos and proofs of concept. A smaller group is pushing real workloads to production—and running into very real challenges around infrastructure efficiency, runaway cloud costs, and reliability at scale.

This session is for engineers and platform teams moving beyond experimentation and building AI systems that actually hold up in production.

// Bio

Ioana Apetrei

Ioana is a Senior Product Manager at CAST AI, leading the AI Enabler product, an AI Gateway platform for cost-effective LLM infrastructure deployment. She brings 12 years of experience building B2C and B2B products reaching over 10 million users. Outside of work, she enjoys assembling puzzles and LEGOs and watching motorsports.

Igor Šušić

Igor is a founding Machine Learning Engineer at CAST AI’s AI Enabler, where he focuses on optimizing inference and training at scale. With a strong background in Natural Language Processing (NLP) and Recommender Systems, Igor has been tackling the challenges of large-scale model optimization long before transformers became mainstream. Prior to CAST AI, he worked at industry leaders like Bloomreach and Infobip, where he contributed to the development and deployment of large-scale AI and personalization systems from the early days of the field.

// Related Links

Website: https://cast.ai/

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Rahul Raja is a Staff Software Engineer at LinkedIn, working on large-scale search infrastructure, information retrieval systems, and integrating AI/ML to improve ranking and semantic search experiences.

The Future of Information Retrieval: From Dense Vectors to Cognitive Search // MLOps Podcast #362 with Rahul Raja, Staff Software Engineer at LinkedIn

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// Abstract

Information Retrieval is evolving from keyword matching to intelligent, vector-based understanding. In this talk, Rahul Raja explores how dense retrieval, vector databases, and hybrid search systems are redefining how modern AI retrieves, ranks, and reasons over information. He discusses how retrieval now powers large language models through Retrieval-Augmented Generation (RAG) and the new MLOps challenges that arise, embedding drift, continuous evaluation, and large-scale vector maintenance.

Looking ahead, the session envisions a future of Cognitive Search, where retrieval systems move beyond recall to genuine reasoning, contextual understanding, and multimodal awareness. Listeners will gain insight into how the next generation of retrieval will bridge semantics, scalability, and intelligence, powering everything from search and recommendations to generative AI.

// BioRahul is a Staff Engineer at LinkedIn, where he focuses on search and deployment systems at scale. Rahul is a graduate from Carnegie Mellon University and has a strong background in building reliable, high-performance infrastructure. He has led many initiatives to improve search relevance and streamline ML deployment workflows.

// Related Links

Website: https://www.linkedin.com/

Coding Agents Conference: https://luma.com/codingagents

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Timestamps:

[00:00] Vector Search for Media

[00:33] RAG and Search Evolution

[04:45] Cognitive vs Semantic Search

[08:26] High Value Search Signals

[16:43] Scaling with Embeddings

[22:37] BM25 Benchmark Bias

[29:00] Video Search Use Cases

[31:21] Context and Search Tradeoff

[35:04] Personal Memory Augmentation

[39:03] Future of Cognitive Search

[44:51] Access Control in Vectors

[49:14] Search Ranking Challenge

[54:43] Hard Search Problems Solved

[58:29] Freshness vs Cost

[1:02:12] Wrap up

Vincent Warmerdam is a Founding Engineer at marimo, working on reinventing Python notebooks as reactive, reproducible, interactive, and Git-friendly environments for data workflows and AI prototyping. He helps build the core marimo notebook platform, pushing its reactive execution model, UI interactivity, and integration with modern development and AI tooling so that notebooks behave like dependable, shareable programs and apps rather than error-prone scratchpads.

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// Abstract

Vincent Warmerdam joins Demetrios fresh off marimo’s acquisition by Weights & Biases—and makes a bold claim: notebooks as we know them are outdated.

They talk Molab (GPU-backed, cloud-hosted notebooks), LLMs that don’t just chat but actually fix your SQL and debug your code, and why most data folks are consuming tools instead of experimenting. Vincent argues we should stop treating notebooks like static scratchpads and start treating them like dynamic apps powered by AI.

It’s a conversation about rethinking workflows, reclaiming creativity, and not outsourcing your brain to the model.

// Bio

Vincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You might know him from tech talks with an attempt to defend common sense over hype in the data space. He is especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has always had a preference to keep calm and check the dataset before flowing tonnes of tensors. He currently works at marimo, where he spends his time rethinking everything related to Python notebooks.

// Related Links

Website: https://marimo.io/

Coding Agent Conference: https://luma.com/codingagents

Hyperbolic GPU Cloud: app.hyperbolic.ai

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Timestamps:

[00:00] Context in Notebooks

[00:24] Acquisition and Team Continuity

[04:43] Coding Agent Conference Announcement!

[05:56] Hyperbolic GPU Cloud Ad

[06:54] marimo and W&B Synergies

[09:31] marimo Cloud Code Support

[12:59] Hardest Code to Generate

[16:22] Trough of Disillusionment

[20:38] Agent Interaction in Notebooks

[25:41] Wrap up

Ereli Eran is the Founding Engineer at 7AI, where he’s focused on building and scaling the company’s agentic AI-driven cybersecurity platform — developing autonomous AI agents that triage alerts, investigate threats, enrich security data, and enable end-to-end automated security operations so human teams can focus on higher-value strategic work.

Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale // MLOps Podcast #361 with Ereli Eran, Founding Engineer at 7AI

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// Abstract

A conversation on how AI coding agents are changing the way we build and operate production systems. We explore the practical boundaries between agentic and deterministic code, strategies for shared responsibility across models, engineering teams, and customers, and how to evaluate agent performance at scale. Topics include production quality gates, safety and cost tradeoffs, managing long-tail failures, and deployment patterns that let you ship agents with confidence.

// Bio

Ereli Eran is a founding engineer at 7AI, where he builds agentic AI systems for security operations and the production infrastructure that powers them. His work spans the full stack - from designing experiment frameworks for LLM-based alert investigation to architecting secure multi-tenant systems with proper authentication boundaries. Previously, he worked in data science and software engineering roles at Stripe, VMware Carbon Black, and was an early employee of Ravelin and Normalyze.

// Related Links

Website: https://7ai.com/

Coding Agents Conference: https://luma.com/codingagents

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Timestamps:

[00:00] Language Sensitivity in Reasoning

[00:25] Value of Claude Code

[01:54] AI in Security Workflows

[06:21] Agentic Systems Failures

[12:50] Progressive Disclosure in Voice Agents

[16:39] LLM vs Classic ML

[19:44] Hybrid Approach to Fraud

[25:58] Debugging with User Feedback

[33:52] Prompts as Code

[42:07] LLM Security Workflow

[45:10] Shared Memory in Security

[49:11] Common Agent Failure Modes

[53:34] Wrap up

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/ Abstract

As AI moves beyond the cloud and simulation, the next frontier is Physical AI: systems that can perceive, understand, and act within real-world environments in real time. In this conversation, Nick Gillian, Co-Founder and CTO of Archetype AI, explores what it actually takes to turn raw sensor and video data into reliable, deployable intelligence.

Drawing on his experience building Google’s Soli and Jacquard and now leading development of Newton, a foundational model for Physical AI, Nick discusses how real-time physical understanding changes what’s possible across safety monitoring, infrastructure, and human–machine interaction. He’ll share lessons learned translating advanced research into products that operate safely in dynamic environments, and why many organizations underestimate the challenges and opportunities of AI in the physical world.

// Bio

Nick Gillian, Ph.D., is Co-Founder and CTO of Archetype AI with over 15 years of experience turning advanced AI and interaction research into real-world products. At Archetype, he leads the AI and engineering teams behind Newton—a first-of-its-kind Physical AI foundational model that can perceive, understand, and reason about the physical world. Before co-founding Archetype, Nick was a Senior Staff Machine Learning Engineer at Google and a researcher at MIT, where he developed AI and ML methods for real-time sensor understanding. At Google’s Advanced Technology and Projects group, he led machine learning research that powered breakthrough products like Soli radar and Jacquard, and helped advance sensing algorithms across Pixel, Nest, and wearable devices.

// Related Links

Website: https://www.archetypeai.io/https://www.archetypeai.io/blog/timefusion-newton https://www.nature.com/articles/s41598-023-44714-2https://www.youtube.com/watch?v=Pow4utY9teU https://www.youtube.com/watch?v=uE0jjdzwe9w https://arxiv.org/abs/2410.14724

Coding Agents Conference: https://luma.com/codingagents

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Timestamps:[00:00] Physical Agent Framework[00:56] Physical AI Clarification[06:53] Building a Repair Model[12:41] World Models and LLMs[17:17] Data Weighting Strategies[24:19] Data Diversity vs Quantity[38:30] R&D and Product Creation[41:22] Construction Site Data Shipping[50:33] Wrap up

Kris Beevers is the CEO at NetBox Labs, working on turning NetBox into the system of record and automation backbone for modern and AI-driven infrastructure.

Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth // MLOps Podcast #359 with Kris Beevers, CEO of NetBox Labs

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// Abstract

Hundreds of neocloud operators and "AI Factory" builders have emerged to serve the insatiable demand for AI infrastructure. These teams are compressing the design, build, deploy, operate, scale cycle of their infrastructures down to months, while managing massive footprints with lean teams. How? By applying modern intent-driven infrastructure automation principles to greenfield deployments. We'll explore how these teams carry design intent through to production, and how operating and automating around consistent infrastructure data is compressing "time to first train".

// Bio

Kris Beevers is the Co-founder and CEO of NetBox Labs. NetBox is used by nearly every Neocloud and AI datacenter to manage their networks and infrastructure. Kris is an engineer at heart and by background, and loves the leverage infrastructure innovation creates to accelerate technology and empower engineers to do their best work. A serial entrepreneur, Kris has founded and helped lead multiple other successful businesses in the internet and network infrastructure. Most recently, he co-founded and led NS1, which was acquired by IBM in 2023. He holds a Ph.D. in Computer Science from Rensselaer Polytechnic Institute and is based in New Jersey.

// Related Links

Website: https://netboxlabs.com/

Coding Agents Conference: https://luma.com/codingagents

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Timestamps:

[00:00] Observability and Delta Analysis

[00:26] New World Exploration

[04:06] Bottlenecks in AI Infrastructure

[13:37] Data Center Optimization Challenges

[19:58] Tech Stack Breakdown

[25:26] Data Center Design Principles

[31:32] Constraints and Automation in Design

[40:00] Complexity in Data Centers

[45:02] GPU Cloud Landscape

[50:24] Data Centers in Containers

[57:45] Observability Beyond Software

[1:04:43] Tighter Integrations vs NetBox

[1:06:47] Wrap up

Mike Oaten is the Founder and CEO of TIKOS, working on building AI assurance, explainability, and trustworthy AI infrastructure, helping organizations test, monitor, and govern AI models and systems to make them transparent, fair, robust, and compliant with emerging regulations.

Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces // MLOps Podcast #358 with Mike Oaten, Founder and CEO of TIKOS

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

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// Abstract

As AI models move into high-stakes environments like Defence and Financial Services, standard input/output testing, evals, and monitoring are becoming dangerously insufficient. To achieve true compliance, MLOps teams need to access and analyse the internal reasoning of their models to achieve compliance with the EU AI Act, NIST AI RMF, and other requirements.

In this session, Mike introduces the company's patent-pending AI assurance technology that moves beyond statistical proxies. He will break down the architecture of the Synapses Logger, a patent-pending technology that embeds directly into the neural activation flow to capture weights, activations, and activation paths in real-time.

// Bio

Mike Oaten serves as the CEO of TIKOS, leading the company’s mission to progress trustworthy AI through unique, high-performance AI model assurance technology. A seasoned technical and data entrepreneur, Mike brings experience from successfully co-founding and exiting two previous data science startups: Riskopy Inc. (acquired by Nasdaq-listed Coupa Software in 2017) and Regulation Technologies Limited (acquired by mnAi Data Solutions in 2022).

Mike's expertise spans data, analytics, and ML product and governance leadership. At TIKOS, Mike leads a VC-backed team developing technology to test and monitor deep-learning models in high-stakes environments, such as defence and financial services, so they comply with the stringent new laws and regulations.

// Related Links

Website: https://tikos.tech/

LLM guardrails: https://medium.com/tikos-tech/your-llm-output-is-confidently-wrong-heres-how-to-fix-it-08194fdf92b9

Model Bias: https://medium.com/tikos-tech/from-hints-to-hard-evidence-finally-how-to-find-and-fix-model-bias-in-dnns-2553b072fd83

Model Robustness: https://medium.com/tikos-tech/tikos-spots-neural-network-weaknesses-before-they-fail-the-iris-dataset-b079265c04da

GPU Optimisation: https://medium.com/tikos-tech/400x-performance-a-lightweight-open-source-python-cuda-utility-to-break-vram-barriers-d545e5b6492f

Hyperbolic GPU Cloud: app.hyperbolic.ai.

Coding Agents Conference: https://luma.com/codingagents

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Timestamps:

[00:00] Regulations as Opportunity

[00:25] Regulation Compliance Fun

[02:49] AI Act Layers Explained

[05:19] Observability in Systems vs ML

[09:05] Risk Transfer in AI

[11:26] LLMs and Model Approval

[14:53] LLMs in Finance

[17:17] Hyperbolic GPU Cloud Ad

[18:16] Stakeholder Alignment and Tech

[22:20] AI in Regulated Environments

[28:55] Autonomous Boat Regulations

[34:20] Data Compliance Mapping

[39:11] Data Capture Strategy

[41:13] EU AI Act Insights

[44:52] Wrap up

[45:45] Join the Coding Agents Conference!

Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, working on AI-powered creator and learning experiences, including intelligent tutoring, content automation, and multilingual localization at scale.

Join us at Coding Agents: The AI Driven Developer Conference - https://luma.com/codingagents

MLOps GPU Guide: ⁠https://go.mlops.community/gpuguide

Join the Community:

https://go.mlops.community/YTJoinIn

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

// Abstract

“Agent as a product” sounds like hype, until Hotmart turns creators’ content into AI businesses that actually work.

// Bio

Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, where he leads efforts in applied AI, machine learning, and generative technologies to power intelligent experiences for creators and learners. He holds an MSc in Computer Science with a focus on artificial intelligence and is also a co-founder of Data Hackers, a prominent data science and AI community in Brazil. Paulo regularly speaks and publishes on topics spanning data science, ML infrastructure, and AI innovation.

// Related LinksWebsite: paulovasconcellos.com.br

Coding Agent - Virtual Conference: https://home.mlops.community/home/events/coding-agents-virtual

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Connect with Paulo on LinkedIn: /paulovasconcellos/

Timestamps:

[00:00] Hotmart Data Science Challenges

[02:38] LLMs vs spaCy

[11:38] Use Cases in Production

[19:04] Coding Agents Virtual Conference Announcement!

[29:27] ML to AI Product Shift

[34:49] Tool-Augmented Agent Approach

[38:28] MLOps GPU Guide

[41:24] AI Use Cases at Hotmart

[49:34] Agent Tool Access Explained

[51:04] MLOps Community Gratitude

[53:22] Wrap up

Wilder Lopes is the CEO and Founder of Ogre.run, working on AI-driven dependency resolution and reproducible code execution across environments.How Universal Resource Management Transforms AI Infrastructure Economics // MLOps Podcast #357 with Wilder Lopes, CEO / Founder of Ogre.runJoin the Community:

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

// AbstractEnterprise organizations face a critical paradox in AI deployment: while 52% struggle to access needed GPU resources with 6-12 month waitlists, 83% of existing CPU capacity sits idle. This talk introduces an approach to AI infrastructure optimization through universal resource management that reshapes applications to run efficiently on any available hardware—CPUs, GPUs, or accelerators.We explore how code reshaping technology can unlock the untapped potential of enterprise computing infrastructure, enabling organizations to serve 2-3x more workloads while dramatically reducing dependency on scarce GPU resources. The presentation demonstrates why CPUs often outperform GPUs for memory-intensive AI workloads, offering superior cost-effectiveness and immediate availability without architectural complexity.// BioWilder Lopes is a second-time founder, developer, and research engineer focused on building practical infrastructure for developers. He is currently building Ogre.run, an AI agent designed to solve code reproducibility.Ogre enables developers to package source code into fully reproducible environments in seconds. Unlike traditional tools that require extensive manual setup, Ogre uses AI to analyze codebases and automatically generate the artifacts needed to make code run reliably on any machine. The result is faster development workflows and applications that work out of the box, anywhere.// Related LinksWebsite: https://ogre.runhttps://lopes.aihttps://substack.com/@wilderlopes https://youtu.be/YCWkUub5x8c?si=7RPKqRhu0Uf9LTql

~~~~~~~~ ✌️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: /dpbrinkmConnect with Wilder on LinkedIn: /wilderlopes/Timestamps:[00:00] Secondhand Data Centers Challenges[00:27] AI Hardware Optimization Debate[03:40] LLMs on Older Hardware[07:15] CXL Tradeoffs[12:04] LLM on CPU Constraints[17:07] Leveraging Existing Hardware[22:31] Inference Chips Overview[27:57] Fundamental Innovation in AI[30:22] GPU CPU Combinations[40:19] AI Hardware Challenges[43:21] AI Perception Divide[47:25] Wrap up

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Shoutout to Databricks for powering this MLOps Podcast episode.

// Abstract

MLflow isn’t just for data scientists anymore—and pretending it is is holding teams back. Corey Zumar, Jules Damji, and Danny Chiao break down how MLflow is being rebuilt for GenAI, agents, and real production systems where evals are messy, memory is risky, and governance actually matters. The takeaway: if your AI stack treats agents like fancy chatbots or splits ML and software tooling, you’re already behind.

// Bio

Corey Zumar

Corey has been working as a Software Engineer at Databricks for the last 4 years and has been an active contributor to and maintainer of MLflow since its first release.

Jules Damji

Jules is a developer advocate at Databricks Inc., an MLflow and Apache Spark™ contributor, and Learning Spark, 2nd Edition coauthor. He is a hands-on developer with over 25 years of experience. He has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, Anyscale, and Databricks, building large-scale distributed systems. He holds a B.Sc. and M.Sc. in computer science (from Oregon State University and Cal State, Chico, respectively) and an MA in political advocacy and communication (from Johns Hopkins University)

Danny Chiao

Danny is an engineering lead at Databricks, leading efforts around data observability (quality, data classification). Previously, Danny led efforts at Tecton (+ Feast, an open source feature store) and Google to build ML infrastructure and large-scale ML-powered features. Danny holds a Bachelor’s Degree in Computer Science from MIT.

// Related Links

Website: https://mlflow.org/

https://www.databricks.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Corey on LinkedIn: /corey-zumar/

Connect with Jules on LinkedIn: /dmatrix/

Connect with Danny on LinkedIn: /danny-chiao/

Timestamps:

[00:00] MLflow Open Source Focus

[00:49] MLflow Agents in Production

[00:00] AI UX Design Patterns

[12:19] Context Management in Chat

[19:24] Human Feedback in MLflow

[24:37] Prompt Entropy and Optimization

[30:55] Evolving MLFlow Personas

[36:27] Persona Expansion vs Separation

[47:27] Product Ecosystem Design

[54:03] PII vs Business Sensitivity

[57:51] Wrap up

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

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MLOps GPU Guide: https://go.mlops.community/gpuguide

// Abstract

Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.

// Bio

Euro Beinat

Euro is a technology executive and entrepreneur specializing in data science, machine learning, and AI. He works with global corporations and startups to build data- and ML-driven products and businesses. His current focus is on Generative AI and the use of AI as a tool for invention and innovation.

Mert Öztekin

Mert is the current Chief Technology Officer at Just Eat Takeaway.com with previous experience as a CTO at Delivery Hero Germany GmbH, Director of Engineering at Delivery Hero, and IT Manager at yemeksepeti.com. They have a background in software engineering, system-business analysis, and project management, with a master's degree in Computer Engineering. Mert has also worked as an IT Project Team Lead and has experience in managing mobile teams and global expansions in the online food ordering industry.

// Related Links

Website: https://www.prosus.com/

Website: https://justeattakeaway.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Euro on LinkedIn: /eurobeinat/

Connect with Mert on LinkedIn: /mertoztekin/

Timestamps:

[00:00] AI Transformation Challenges

[00:29] AI Productivity

[04:30] Developer Tool Freedom

[09:40] AI Alignment Bottleneck

[22:17] Exploring Agent Potential

[25:59] Governance of AI Agents

[33:24] Shadow AI Governance

[40:57] AI Budgeting for Growth

[46:27] MLOps GPU Guide announcement!

Zengyi Qin is the Founder of the OpenAGI Foundation, working on computer-use models and open, agent-centric AI infrastructure.

Computers that Think and Take Actions for You, Zengy Qin // MLOps Podcast #355

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// Abstract

What if the computer itself can think and take actions for you? You just give it a goal, and it performs every click, type, drag, and gets work done across the desktop and web. In this talk, Zengyi reveals the breakthrough technology that his company OpenAGI is developing: AI that can use computers like humans do. He talks about how his team developed the model, why it outperforms similar models from OpenAI and Google, and its wide use cases across different domains.

// Related Links

Website: https://www.qinzy.tech/

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Zengyi on LinkedIn: /qinzy/

Timestamps:

[00:00] AI and Human Interaction

[00:30] Zengyi's story

[08:19] Why Expensive Models Lost

[06:30] Bigger Models Are Lazy

[10:24] Training Computer-Use vs LLMs

[13:53] World Models and Sandboxes

[19:42] Dealing with Non-Stationary States

[23:56] Training with Software

[26:44] Sandbox Training Process

[41:33] Infrastructure for Computer Models

[44:36] Wrap up

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[https://shop.mlops.community/]

And huge thanks to Chroma for hosting us in their recording studio

// Abstract

Feature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.

// Related Links

Website: https://zipline.ai/

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

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Varant on LinkedIn: /vzanoyan/

Connect with Nikhil on LinkedIn: /nikhilsimha/

Timestamps:

[00:00] Feature Platform Insights

[02:00] Zipline and Feature Stores

[05:19] Cronon and Zipline Origins

[10:49] Feast and Feather Comparison

[13:27] Open source challenges

[20:52] Zipline and Iceberg Integration

[23:54] Airbnb Agent Systems

[28:16] Features vs Embeddings

[29:07] Wrap up

Alex Salazar is the CEO and Co-Founder of Arcade.dev, working on secure AI agents and real-world automation integrations.

Chiara Caratelli is a Data Scientist at Prosus Group, working on AI agents, web automation, and evaluation of robust multimodal models.

Join the Community:

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MLOps GPU Guide: ⁠

https://go.mlops.community/gpuguide

// Abstract

Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.

// Bio

Chiara Caratelli

Chiara is a Data Scientist at Prosus, where she develops AI-driven solutions with a focus on AI agents, multimodal models, and new user experiences. With a PhD in Computational Science and a background in machine learning engineering and data science, she has worked on deploying AI-powered applications at scale, collaborating with Prosus portfolio companies to drive real-world impact.

Beyond her work at Prosus, she enjoys experimenting with generative AI and art. She is also an avid climber and book reader, always eager to explore new ideas and share knowledge with the AI and ML community.

Alex Salazar

Alex is the CEO and co-founder of Arcade.dev, the unified agent action platform that makes AI agents production-ready. Previously, Salazar co-founded Stormpath, the first authentication API for developers, which was acquired by Okta. At Okta, he led developer products, accounting for 25% of total bookings, and launched a new auth-centric proxy server product that reached $9M in revenue within a year. He also managed Okta's network of over 7,000 auth integrations. Alex holds a computer science degree from Georgia Tech and an MBA from Stanford University.

// Related Links

Website: https://www.prosus.com/

Website: https://www.arcade.dev/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alex on LinkedIn: /alexsalazar/

Connect with Chiara on LinkedIn: /chiara-caratelli/

Timestamps:

[00:00] Intro

[00:15] Insights from iFood

[06:22] API vs agent intention

[09:45] Tool definition clarity

[15:37] Preemptive context loading

[27:50] Contextualizing agent data

[33:27] Prompt bloat in payments

[41:33] Agent building evolution

[50:09] Agent program scalability

[55:29] Why multi-agent is a dead end

[56:17] Wrap up

Jonathan Wall is the CEO at Runloop.ai, working on enterprise-grade infrastructure and execution environments for AI coding agents.

The Future of AI Agents is Sandboxed // MLOps Podcast #353 with Jonathan Wall, CEO at Runloop.ai.

Join the Community:

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Shoutout to  @runloop-ai  for powering this MLOps Podcast episode.

// Abstract

Everyone’s arguing about agents. Jonathan Wall says the real fight is about sandboxes, isolation, and why most “agent platforms” are doing it wrong.

// Bio

Jon was the techlead of Google File System, a founding engineer at Google Wallet, and then the founder of Inde, which was acquired by Stripe. He is building Runloop.ai to bridge the production gap for AI Agents by building a one-stop sandbox infrastructure for building, deploying, and refining agents.

// Related Links

Website: runloop.ai

Blogs and content at https://www.runloop.ai/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Jon on LinkedIn: /jonathantwall/

Timestamps:

[00:00] GitHubification of workflows

[00:29] Sandbox definitions explained

[04:47] Agent setup explanation

[08:03] Sandbox vs API agent

[13:51] Resource usage in sandbox

[22:50] Agent evaluation setup

[28:08] Failure cases value

[31:06] Sandbox isolation vs multi-tenancy

[36:14] Frameworks vs Harnesses

[39:02] Langraph vs Harness comparison

[43:22] Agent flexibility and verification

[52:51] Training data focus

[57:10] Wrap up

Simba Khadder is the founder and CEO of Featureform, now at Redis, working on real-time feature orchestration and building a context engine for AI and agents.

Context Engineering 2.0, Simba Khadder // MLOps Podcast #352

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// Abstract

Feature stores aren’t dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn’t models, it’s context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.

// Bio

Simba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.

// Related Links

Website: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Simba on LinkedIn: /simba-k/

Timestamps:

[00:00] Context engineering explanation

[00:25] MLOps and feature stores

[03:36] Selling a company experience

[06:34] Redis feature store evolution

[12:42] Embedding hub

[20:42] Human vs agent semantics

[26:41] Enrich MCP data flow

[29:55] Data understanding and embeddings

[35:18] Search and context tools

[39:45] MCP explained without hype

[45:15] Wrap up

Satish Bhambri is a Sr Data Scientist at Walmart Labs, working on large-scale recommendation systems and conversational AI, including RAG-powered GroceryBot agents, vector-search personalization, and transformer-based ad relevance models.

Join the Community:

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// Abstract

The MLOps Community Podcast features Satish Bhambri, Senior Data Scientist with the Personalization and Ranking team at Walmart Labs and one of the emerging leaders in applied AI, in its newest episode. Satish has quietly built one of the most diverse and impactful AI portfolios in his field, spanning quantum computing, deep learning, astrophysics, computer vision, NLP, fraud detection, and enterprise-scale recommendation systems. Bhambri's nearly a decade of research across deep learning, astrophysics, quantum computing, NLP, and computer vision culminated in over 10 peer-reviewed publications released in 2025 through IEEE and Springer, and his early papers are indexed by NASA ADS and Harvard SAO, marking the start of his long-term research arc. He also holds a patent for an AI-powered smart grid optimization framework that integrates deep learning, real-time IoT sensing, and adaptive control algorithms to improve grid stability and efficiency, a demonstration of his original, high-impact contributions to intelligent infrastructure.

Bhambri leads personalization and ranking initiatives at Walmart Labs, where his AI systems serve more than (5% of the world’s population) 531 million users every month, roughly based on traffic data. His work with Transformers, Vision-Language Models, RAG and agentic-RAG systems, and GPU-accelerated pipelines has driven significant improvements in scale and performance, including increases in ad engagement, faster compute by and improved recommendation diversity.

Satish is a Distinguished Fellow & Assessor at the Soft Computing Research Society (SCRS), a reviewer for IEEE and Springer, and has served as a judge and program evaluator for several elite platforms. He was invited to the NeurIPS Program Judge Committee, the most prestigious AI conference in the world, and to evaluate innovations for DeepInvent AI, where he reviews high-impact research and commercialization efforts. He has also judged Y Combinator Startup Hackathons, evaluating pitches for an accelerator that produced companies like Airbnb, Stripe, Coinbase, Instacart, and Reddit.

Before Walmart, Satish built supply-chain intelligence systems at BlueYonder that reduced ETA errors and saved retailers millions while also bringing containers to the production pipeline. Earlier, at ASU’s School of Earth & Space Exploration, he collaborated with astrophysicists on galaxy emission simulations, radio burst detection, and dark matter modeling, including work alongside Dr. Lawrence Krauss, Dr. Karen Olsen, and Dr. Adam Beardsley.

On the podcast, Bhambri discusses the evolution of deep learning architectures from RNNs and CNNs to transformers and agentic RAG systems, the design of production-grade AI architectures with examples, and his long-term vision for intelligent systems that bridge research and real-world impact. and the engineering principles behind building production-grade AI at a global scale.

// Related Links

Papers: https://scholar.google.com/citations?user=2cpV5GUAAAAJ&hl=en

Patent: https://search.ipindia.gov.in/DesignApplicationStatus

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// Abstract

Sierra’s Zack Reneau-Wedeen claims we’re building AI all wrong and that “context engineering,” not bigger models, is where the real breakthroughs will come from. In this episode, he and Demetrios Brinkmann unpack why AI behaves more like a moody coworker than traditional software, why testing it with real-world chaos (noise, accents, abuse, even bad mics) matters, and how Sierra’s simulations and model “constellations” aim to fix the industry’s reliability problems. They even argue that decision trees are dead, replaced by goals, guardrails, and speculative execution tricks that make voice AI actually usable. Plus: how Sierra trains grads to become product-engineering hybrids, and why obsessing over customers might be the only way AI agents stop disappointing everyone.

// Related Links

Website: https://www.zackrw.com/

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Timestamps:

[00:00] Electron cloud vs energy levels

[03:47] Simulation vs red teaming

[06:51] Access control in models

[10:12] Voice vs text simulations

[13:12] Speaker-adaptive turn-taking

[18:26] Accents and model behavior

[23:52] Outcome-based pricing risks

[31:40] AI cross-pollination strategies

[41:26] Ensemble of models explanation

[46:47] Real-time agents vs decision trees

[50:15] Code and no-code mix

[54:04] Goals and guardrails explained

[56:23] Wrap up

[57:31] APX program!

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Shoutout to Airia for powering this MLOps Podcast episode.

// Abstract

Spencer Reagan thinks it might be, and he’s not shy about saying so. In this episode, he and Demetrios Brinkmann get real about the messy, over-engineered state of agent systems, why LLMs still struggle in the wild, and how enterprises keep tripping over their own data chaos. They unpack red-teaming, security headaches, and the uncomfortable truth that most “AI platforms” still don’t scale. If you want a sharp, no-fluff take on where agents are actually headed, this one’s worth a listen.

// Bio

Passionate about technology, software, and building products that improve people's lives.

// Related Links

Website: https://airia.com/

Machine Learning, AI Agents, and Autonomy // Egor Kraev // MLOps Podcast #282 - https://youtu.be/zte3QDbQSek

Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // MLOps Podcast #281 - https://youtu.be/1ouSuBETkdA

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Timestamps:

[00:00] AI industry future

[00:55] Use cases in software

[05:44] LLMs for data normalization

[11:02] ROI and overengineering

[15:58] Street width history

[20:58] High ROI examples

[25:16] AI building challenges

[33:37] Budget control challenges

[39:30] Airia Orchestration platform

[46:25] Agent evaluation breakdown

[53:48] Wrap up

Thanks to Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.

Abstract //

The discussion centers on highly technical yet practical themes, such as the use of advanced post-training techniques like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT) to ensure LLMs maintain stability while specializing for e-commerce domains. We compare the implementation challenges of Computer-Using Agents in automating legacy enterprise systems versus the stability issues faced by conversational agents when inputs become unpredictable in production. We will analyze the role of cloud infrastructure in supporting the continuous, iterative training loops required by Reinforcement Learning-based agents for e-commerce!

Bio //

Paul van der Boor (Panel Host) //

Paul van der Boor is a Senior Director of Data Science at Prosus and a member of its internal AI group.

Arushi Jain (Panelist) //

Arushi is a Senior Applied Scientist at Microsoft, working on LLM post-training for Computer-Using Agent (CUA) through Reinforcement Learning. She previously completed Microsoft’s competitive 2-year AI Rotational Program (MAIDAP), building and shipping AI-powered features across four product teams.

She holds a Master’s in Machine Learning from the University of Michigan, Ann Arbor, and a Dual Degree in Economics from IIT Kanpur. At Michigan, she led the NLG efforts for the Alexa Prize Team, securing a $250K research grant to develop a personalized, active-listening socialbot. Her research spans collaborations with Rutgers School of Information, Virginia Tech’s Economics Department, and UCLA’s Center for Digital Behavior.

Beyond her technical work, Arushi is a passionate advocate for gender equity in AI. She leads the Women in Data Science (WiDS) Cambridge community, scaling participation in her ML workshops from 25 women in 2020 to 100+ in 2025—empowering women and non-binary technologists through education and mentorship.

Swati Bhatia //

Passionate about building and investing in cutting-edge technology to drive positive impact.

Currently shaping the future of AI/ML at Google Cloud.

10+ years of global experience across the U.S, EMEA, and India in product, strategy & venture capital (Google, Uber, BCG, Morpheus Ventures).

Audi Liu //

I’m passionate about making AI more useful and safe.

Why? Because AI will be ubiquitous in every workflow, powering our lives just like how electricity revolutionized our society - It’s pivotal we get it right.

At Inworld AI, we believe all future software will be powered by voice. As a Sr Product Manager at Inworld, I'm focused on building a real-time voice API that empowers developers to create engaging, human-like experiences. Inworld offers state-of-the-art voice AI at a radically accessible price - No. 1 on Hugging Face and Artificial Analysis, instant voice cloning, rich multilingual support, real-time streaming, and emotion plus non-verbal control, all for just $5 per million characters.

Isabella Piratininga //

Experienced Product Leader with over 10 years in the tech industry, shaping impactful solutions across micro-mobility, e-commerce, and leading organizations in the new economy, such as OLX, iFood, and now Nubank. I began my journey as a Product Owner during the early days of modern product management, contributing to pivotal moments like scaling startups, mergers of major tech companies, and driving innovation in digital banking.

My passion lies in solving complex challenges through user-centered product strategies. I believe in creating products that serve as a bridge between user needs and business goals, fostering value and driving growth. At Nubank, I focus on redefining financial experiences and empowering users with accessible and innovative solutions.

Check out all the talks from the conference here: https://go.mlops.community/carzleGet some "I hallucinate more than ChatGPT" t-shirts here: https://go.mlops.community/NL_RY25_Merch

Thanks to  Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.

In this session, Ricky Doar, VP of Solutions at Cursor, shares actionable insights from leading large-scale AI developer tool implementations at the world’s top enterprises. Drawing on field experience with organizations at the forefront of transformation, Ricky highlights key best practices, observed power-user patterns, and deployment strategies that maximize value and ensure smooth rollout. Learn what distinguishes high-performing teams, how tailored onboarding accelerates adoption, and which support resources matter most for driving enterprise-wide success.

A Prosus | MLOps Community Production

Check out all the talks from the conference here: ⁠https://go.mlops.community/carzle⁠Get some "I hallucinate more than ChatGPT" t-shirts here: ⁠https://go.mlops.community/NL_RY25_Merch

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// Abstract

Today’s foundation models excel at text and images—but they miss the relationships that define how the world works. In every enterprise, value emerges from connections: customers to products, suppliers to shipments, molecules to targets. This talk introduces Relational Foundation Models (RFMs)—a new class of models that reason over interactions, not just data points. Drawing on advances in graph neural networks and large-scale ML systems, I’ll show how RFMs capture structure, enable richer reasoning, and deliver measurable business impact. Audience will learn where relational modeling drives the biggest wins, how to build the data backbone for it, and how to operationalize these models responsibly and at scale.

// Bio

Jure Leskovec is the co-founder of Kumo.AI, an enterprise AI company pioneering AI foundation models that can reason over structured business data. He is also a Professor of Computer Science at Stanford University and a leading researcher in artificial intelligence, best known for pioneering Graph Neural Networks and creating PyG, the most widely used graph learning toolkit. Previously, Jure served as Chief Scientist at Pinterest and as an investigator at the Chan Zuckerberg BioHub. His research has been widely adopted in industry and government, powering applications at companies such as Meta, Uber, YouTube, Amazon, and more. He has received top awards in AI and data science, including the ACM KDD Innovation Award.

// Related Links

Website: https://cs.stanford.edu/people/jure/

https://www.youtube.com/results?search_query=jure+leskovec

Please watch Jure's keynote:

https://www.youtube.com/watch?v=Rcfhh-V7x2U

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Timestamps:

[00:00] Structured data value

[00:26] Breakdown of ML Claims

[05:04] LLMs vs recommender systems

[10:09] Building a relational model

[15:47] Feature engineering impact

[20:42] Knowledge graph inference

[26:45] Advertising models scale

[32:57] Feature stores evolution

[38:00] Training model compute needs

[42:34] Predictive AI for agents

[45:32] Leveraging faster predictive models

[48:00] Wrap up

Jeff Huber is the CEO of ​Chroma, working on context engineering and building reliable retrieval infrastructure for AI systems.

Context Engineering, Context Rot, & Agentic Search with the CEO of Chroma, Jeff Huber // MLOps Podcast #348.

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// Abstract

Jeff Huber drops some hard truths about “context rot” — the slow decay of AI memory that’s quietly breaking your favorite models. From retrieval chaos to the hidden limits of context windows, he and Demetrios Brinkmann unpack why most AI systems forget what matters and how Chroma is rethinking the entire retrieval stack. It’s a bold look at whether smarter AI means cleaner context — or just better ways to hide the mess.

// Bio

Jeff Huber is the CEO and cofounder of Chroma. Chroma has raised $20M from top investors in Silicon Valley and builds modern search infrastructure for AI.

// Related Links

Website: https://www.trychroma.com/

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Timestamps:

[00:00] AI intelligence context clarity

[00:37] Context rot explanation

[03:02] Benchmarking context windows

[05:09] Breaking down search eras

[10:50] Agent task memory issues

[17:21] Semantic search limitations

[22:54] Context hygiene in AI

[30:15] Chroma on-device functionality

[38:23] Vision for precision systems

[43:07] ML model deployment challenges

[44:17] Wrap up

Brooke Hopkins is the CEO of Coval, a company making voice agents more reliable.

Reliable Voice Agents // MLOps Podcast #347 with Brooke Hopkins, Founder of Coval.

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// Abstract

Voice AI is finally growing up—but not without drama. Brooke Hopkins joins Demetrios Brinkmann to unpack why most “smart” voice systems still feel dumb, what it actually takes to make them reliable, and how startups are quietly outpacing big tech in building the next generation of voice agents.

// Bio

Brooke Hopkins is the founder of Coval, a simulation and evaluation platform for AI agents. She previously led the evaluation job infrastructure at Waymo. There, her team was responsible for the developer tools for launching and running simulations, and she engineered many of the core simulation systems from the ground up.

// Related Links

Website: https://www.coval.dev/

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Connect with Brooke on LinkedIn: /bnhop/

Timestamps:

[00:00] Workshop feedback

[02:21] IVR frustration and transition

[05:06] Voice use cases in business

[11:00] Voice AI reliability challenge

[18:46] Voice AI reliability issues

[24:35] Injecting context

[27:16] Conversation flow analysis

[34:52] AI overgeneralization and confidence

[37:41] Wrap up

Huge thanks to PwC for supporting this episode!

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// Abstract

In today’s data-driven IT landscape, managing ML lifecycles and operations is converging.

On this podcast, we’ll explore how end-to-end ML lifecycle practices extend to proactive, automation-driven IT operations.

We'll discuss key MLOps concepts—CI/CD pipelines, feature stores, model monitoring—and how they power anomaly detection, event correlation, and automated remediation.

// Bio

Rani Radhakrishnan, a Principal at PwC, currently leads the AI Managed Services and Data & Insight teams in PwC US Technology Managed Services.

Rani excels at transforming data into strategic insights, driving informed decision-making, and delivering innovative solutions. Her leadership is marked by a deep understanding of emerging technologies and a commitment to leveraging them for business growth.

Rani’s ability to align and deliver AI solutions with organizational outcomes has established her as a thought leader in the industry.

Her passion for applying technology to solve tough business challenges and dedication to excellence continue to inspire her teams and help drive success for her clients in the rapidly evolving AI landscape.

// Related Links

Website: https://www.pwc.com/us/managedservices

https://www.pwc.com/us/en/tech-effect.html

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Timestamps:

[00:00] Getting to Know Rani

[01:54] Managed services

[03:50] AI usage reflection

[06:21] IT operations and MLOps

[11:23] MLOps and agent deployment

[14:35] Startup challenges in managed services

[16:50] Lift vs practicality in ML

[23:45] Scaling in production

[27:13] Data labeling effectiveness

[29:40] Sustainability considerations

[37:00] Product engineer roles

[40:21] Wrap up

The GPU Uptime Battle // MLOps Podcast #346 with Andy Pernsteiner, Field CTO of VAST Data.Huge thanks to VAST Data for supporting this episode!

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// Abstract

Most AI projects don’t fail because of bad models; they fail because of bad data plumbing. Andy Pernsteiner joins the podcast to talk about what it actually takes to build production-grade AI systems that aren’t held together by brittle ETL scripts and data copies. He unpacks why unifying data - rather than moving it - is key to real-time, secure inference, and how event-driven, Kubernetes-native pipelines are reshaping the way developers build AI applications. It’s a conversation about cutting out the complexity, keeping data live, and building systems smart enough to keep up with your models.

// Bio

Andy is the Field Chief Technology Officer at VAST, helping customers build, deploy, and scale some of the world’s largest and most demanding computing environments.

Andy has spent the past 15 years focused on supporting and building large-scale, high-performance data platform solutions. From humble beginnings as an escalations engineer at pre-IPO Isilon, to leading a team of technical Ninjas at MapR, he’s consistently been in the frontlines solving some of the toughest challenges that customers face when implementing Big Data Analytics and next-generation AI solutions.

// Related Links

Website: www.vastdata.com

https://www.youtube.com/watch?v=HYIEgFyHaxk

https://www.youtube.com/watch?v=RyDHIMniLro

The Mom Test by Rob Fitzpatrick: https://www.momtestbook.com/

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Timestamps:

[00:00] Prototype to production gap

[00:21] AI expectations vs reality

[03:00] Prototype vs production costs

[07:47] Technical debt awareness

[10:13] The Mom Test

[15:40] Chaos engineering

[22:25] Data messiness reflection

[26:50] Small data value

[30:53] Platform engineer mindset shift

[34:26] Gradient description comparison

[38:12] Empathy in MLOps

[45:48] Empathy in Engineering

[51:04] GPU clusters rolling updates

[1:03:14] Checkpointing strategy comparison

[1:09:44] Predictive vs Generative AI

[1:17:51] On Growth, Community, and New Directions

[1:24:21] UX of agents

[1:32:05] Wrap up

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// Abstract

Modern cyber operations can feel opaque. This talk explains—step by step—what a security operations center (SOC) actually does, how telemetry flows in from networks, endpoints, and cloud apps, and what an investigation can credibly reveal about attacker behavior, exposure, and control gaps. We then trace how AI has shown up in the SOC: from rules and classic machine learning for detection to natural-language tools that summarize alerts and turn questions like “show failed logins from new countries in the last 24 hours” into fast database queries. The core of the talk is our next step: agentic investigations. These GenAI agents plan their work, run queries across tools, cite evidence, and draft analyst-grade findings—with guardrails and a human in the loop. We close with what’s next: risk-aware auto-remediation, verifiable knowledge sources, and a practical checklist for adopting these capabilities safely.

// Bio

Dr. Jeff Schwartzentruber holds the position of Sr. Machine Learning Scientist at eSentire – a Canadian cybersecurity company specializing in Managed Detection and Response (MDR). Dr. Schwartzentruber’s primary academic and industry research has been concentrated on solving problems at the intersection of cybersecurity and machine learning (ML). Over his +10-year career, Dr. Schwartzentruber has been involved in applying ML for threat detection and security analytics for several large Canadian financial institutions, public sector organizations (federal), and SME’s. In addition to his private sector work, Dr. Schwartzentruber is also an Adjunct Faculty at Dalhousie University in the Department of Computer Science, a Special Graduate Faculty member with the School of Computer Science at the University of Guelph, and a Sr. Advisor on AI at the Rogers Cyber Secure Catalysts.

// Related Links

Website: https://www.esentire.com/

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// Abstract

David Jayatillake argues that the real battle in data isn’t about AI — it’s about who controls the semantics. In this episode, he calls out how proprietary BI tools quietly lock companies into their ecosystems, making data less open and less useful. David and Demetrios debate whether semantic layers should live in open-source hands and how AI agents might soon replace entire chunks of manual data engineering. From feature stores to LLM-driven analytics, this conversation challenges how we think about ownership, access, and the future of data workflows.

// Bio

Experienced and world-renowned data, technology, and AI leader. Expert in the application of LLMs to the semantic layer.

Writes at davidsj.substack.com about data, leadership, architecture, venture capital, and artificial intelligence.

Two-time co-founder in the data space. Founded Delphi Labs, which focused on applying LLMs to semantic layers to enable data democratization.

Regular data conference, podcast, panel, and webinar speaker.

// Related Links

Website: davidsj.substack.com

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// Abstract

Demetrios Brinkmann talks with Siddharth Bidasaria about Anthropic’s Claude code — how it was built, key features like file tools and Spotify control, and the team’s lean, user-focused approach. They explore testing, subagents, and the future of agentic coding, plus how users are pushing its limits.

// BioSoftware engineer. Founding team of Claude Code. Ex-Robinhood and Rubrik.

// Related Links

Bio: https://sidb.io/

Sid's Blog: https://sidb.io/posts/

I Let An AI Play Pokémon! - Claude plays Pokémon Creator: https://youtu.be/nRHeGJwVP18

How Data Platforms Affect ML & AI // Jake Watson // MLOps Podcast #207: https://youtu.be/xWApMuyct_4

The Agent Landscape - Lessons Learned Putting Agents Into Production: https://youtu.be/lRGldru7ohU

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Timestamps:

[00:00] MCP servers usage creativity

[00:34] Claude's code origin story

[05:17] R&D freedom and tools

[09:08] Model potential discovery

[12:06] Model adaptation strategies

[19:13] Steerability vs pattern alignment

[22:09] Features to delete

[24:12] Moore's law in LLMs

[32:42] Power user surprises

[35:56] Sub-agent evolution insights

[39:54] Agent communication governance

[45:26] At-scale agent coordination

[49:56] Wrap up

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#podcast #aiinfrastructure #aiagents #memory

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// Abstract

While conversations around AI regulations continue to evolve, the responsibility for AI continues to be with developers. In this episode, Marco Palladino, CTO and co-founder of Kong Inc., explores what it means to build and scale AI responsibly when the rulebook is still being written. He explains that infrastructure should be the frontline defense for enforcing governance, security, and reliability in AI deployments. Marco shares how Kong’s technologies, including AI Gateway and AI Manager, help organizations rein in shadow AI, reduce LLM hallucinations, improve observability, and act as the foundation for agentic workflows.

// Bio

Marco Palladino is an inventor, software developer, and internet entrepreneur. As the CTO and co-founder of Kong, he is Kong’s co-author, responsible for the design and delivery of the company’s products, while also providing technical thought leadership around APIs and microservices within both Kong and the external software community. Prior to Kong, Marco co-founded Mashape in 2010, which became the largest API marketplace and was acquired by RapidAPI in 2017.

// Related Links

Website: https://konghq.com/ https://www.youtube.com/watch?v=odpPVeQZjHU https://www.thestack.technology/the-big-interview-kong-cto-marco-palladino/

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Timestamps:

[00:00] Agent-mediated interactions shift

[01:17] Kong connectivity and agents

[04:36] Transcript cleanup request

[08:11] MCP server use cases

[12:37] Agent world possibilities

[15:55] Business communication evolution

[18:55] System optimization

[25:36] AI gateway patterns

[31:30] Investment decision making

[35:54] Building conviction process

[41:34] Polished customer conversation

[46:37] AI gateway R&D future

[50:52] Wrap up

AI Conversations Powered by Prosus Group

Unicorn Mafia won the recent hackathon at Raise Summit and explained to me what they built, including all the tech they used under the hood to make their AI agents work.

Winners:

Charlie Cheesman - Co-founder at 60x.ai

Marissa Liu - Tech Lead, Reporting at Watershed

Ana Shevchenko - Software Engineer II at Spotify

Fergus McKenzie-Wilson - Co-founder at 60x.ai

Alex Choi - Founding Engineer at Medfin

Host:

Demetrios Brinkmann - Founder of MLOps Community

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// Abstract

AI agents are transitioning from experimental stages to performing real work in production; however, they have largely been limited to backend task automation. A critical frontier in this evolution is the on-device AI agent, enabling sophisticated, AI-native experiences directly on mobile and embedded devices. While cloud-based AI faces challenges like constant connectivity demands, increased latency, privacy risks, and high operational costs, on-device breaks through these trade-offs.

We'll delve into the practical side of building and deploying AI agents with “DeliteAI”, an open-source on-device AI agentic framework. We'll explore how lightweight Python runtimes facilitate the seamless orchestration of end-to-end workflows directly on devices, allowing AI/ML teams to define data preprocessing, feature computation, model execution, and post-processing logic independently of frontend code. This architecture empowers agents to adapt to varying tasks and user contexts through an ecosystem of tools natively supported on Android/iOS platforms, handling all the permissions, model lifecycles, and many more.

// Bio

Varun Khare

Varun is the Founder and CEO of NimbleEdge, an AI startup pioneering privacy-first, on-device intelligence. With an academic foundation in AI and neuroscience from UC Berkeley, MPI Frankfurt, and IIT Kanpur, Varun brings deep expertise at the intersection of technology and science. Before founding NimbleEdge, Varun led open-source projects at OpenMined, focusing on privacy-aware AI, and published research in computer vision.

Neeraj Poddar

Neeraj Poddar is the Co-founder and CTO at NimbleEdge. Prior to NimbleEdge, he was the Co-founder of Aspen Mesh, VP of Engineering at Solo.io, and led the Istio open source community. He has worked on various aspects of AI, networking, security, and distributed systems over the span of his career. Neeraj focuses on the application of open source technologies across different industries in terms of scalability and security. When not working on AI, you can find him playing racquetball and gaining back the calories spent playing by trying out new restaurants.

// Related Links

Website: https://www.nimbleedge.com/

https://www.nimbleedge.com/blog/why-ai-is-not-working-for-you

https://www.nimbleedge.com/blog/state-of-on-device-ai

https://www.youtube.com/watch?v=Qqj_Nl2MihE

https://www.linkedin.com/events/7343237917982527488/comments/

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Timestamps:

[00:00] On-device AI skepticism

[02:47] Word suggestion for AI

[06:40] Optimizing unique challenges

[13:39] LLM on-device challenges

[20:34] Agent overlord tension

[23:56] AI app constraints

[29:23] Siri limitations and trust gap

[32:01] Voice-driven app privacy

[35:49] Platform lock-in vs aggregation

[42:26] On-device AI optimizations

[45:38] Wrap up

AI Conversations Powered by Prosus Group 

Your AI agent isn’t failing because it’s dumb—it’s failing because you refuse to test it. Chiara Caratelli cuts through the hype to show why evaluations—not bigger models or fancier prompts—decide whether agents succeed in the real world. If you’re not stress-testing, simulating, and iterating on failures, you’re not building AI—you’re shipping experiments disguised as products.

Guest speaker: Chiara Caratelli - Data Scientist @ Prosus Group

Host: Demetrios Brinkmann - Founder of MLOps Community

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// Abstract

Managing data on Object Stores has been a painful affair. Users had to choose between data swamp chaos or a maze of metadata files with catalog servers on top.

DuckLake is a new paradigm for managing data on object stores: First, it uses classical SQL data management systems to manage metadata. Second, actual data is stored in Parquet files on pretty arbitrary storage. Third, processing queries is done client-side, or anywhere really. DuckDB is the first system to integrate with DuckLake using an extension with the same name.

Conceptually, DuckLake enables central control over truth while decentralizing compute and storage entirely. DuckLake turns data warehouse architecture upside down by departing from the integrated metadata/compute layer towards a fully disconnected operation with only centralized metadata. For the first time, DuckLake allows a “multi-player” experience with DuckDB, where computation stays fully local, but transactional control is centralized.

// Bio

Hannes Mühleisen 🔈 is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs. He is a senior researcher at the Centrum Wiskunde & Informatica (CWI) in Amsterdam. He is also Professor of Data Engineering at Radboud University Nijmegen.

// Related Links

Website: https://hannes.muehleisen.orgUnleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 - https://youtu.be/pF8zTI867EI

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Timestamps:

[00:00] Spooky ease in tech

[00:29] DuckDB and DuckLake

[07:50] Pain vs trust factors

[13:12] Prioritizing project features

[16:16] Platform growth tension

[22:06] Building principles

[25:26] OSS vs system reliability

[30:27] Creative uses of DuckDB

[35:35] Tecton product strategy

[43:30] Mindset shift

[52:25] DuckDB future shifts

[55:37] Wrap up

AI Conversations Powered by Prosus Group 

Russ d'Sa shares how LiveKit went from a small open-source project during the pandemic to powering voice interfaces for giants like OpenAI. He talks about the turning point when LiveKit teamed up on ChatGPT’s voice features, the challenges of making AI sound human, and why voice could be the future of multimodal AI. It’s a story of chance, big shifts, and building the backbone of tomorrow’s AI.

Guest speakers: Russ Dsa - CEO of LiveKit

Host: Demetrios Brinkmann - Founder of MLOps Community

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#voiceagents #podcast

AI Conversations Powered by Prosus Group. 

Craig Tavares, COO of Buzz High Performance Compute, shares lessons from building GPU cloud infrastructure worldwide. He stresses the role of sovereign mandates, renewable power, and modular cooling in scaling data centers, while highlighting NVIDIA partnerships and orchestration as key to sustaining AI workloads.

Guest speaker: Craig Tavares - COO of Buzz HPC

Host: Demetrios Brinkmann - Founder of MLOps Community

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// Abstract

For better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation, we will discuss ways that enterprise-scale organizations are addressing these challenges to safely and securely build these development environments.

// Bio

Hudson Buzby is a solution engineer with an emphasis on MLOps, LLMOps, Big Data, and Distributed Systems, leveraging his expertise to help organizations optimize their machine learning operations and large language model deployments. His role involves providing technical solutions and guidance to enhance the efficiency and effectiveness of AI-driven projects.

// Related Links

https://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg

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Timestamps:

[00:00] Value of Centralized Gateway

[00:35] Point Break vs Big Lebowski

[01:47] AI adoption failure stats

[05:12] ML vs Generative AI

[12:04] LLM adoption in enterprise

[18:08] MLOps Community alternative

[23:43] AI governance challenges

[27:39] Organizational debt comparison

[31:41] AI tool sprawl

[35:59] MLOps to platform evolution

[40:56] MLOps then vs now

[49:48] Model trust and safety

[52:19] AI model effectiveness

[55:54] Product discovery process

[58:38] Wrap up

AI Conversations Powered by Prosus Group 

Nishikant Dhanuka talks about what it really takes to make AI agents useful—especially in e-commerce and productivity. From making them smarter with context (like user history and real-time data) to mixing chat and UI for smoother interactions, he breaks down what’s working and what’s not. He also shares why evals matter, how to test with real users, and why AI only succeeds when it actually makes life easier, not more complicated.

Guest speaker: Nishikant Dhanuka - Senior Director of AI at Prosus Group

Host: Demetrios Brinkmann - Founder of MLOps Community

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#contextengineering #aiengineer #aiinfrastructure #podcast

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// Abstract

We’re entering a new era in marketing—one powered by AI agents, not just analysts. The rise of tools like Clay, Karrot.ai, 6sense, and Mutiny is reshaping how go-to-market (GTM) teams operate, making room for a new kind of operator: the GTM engineer. This hybrid role blends technical fluency with growth strategy, leveraging APIs, automation, and AI to orchestrate hyper-personalized, scalable campaigns. No longer just marketers, today’s GTM teams are builders—connecting data, deploying agents, and fine-tuning workflows in real time to meet buyers where they are. This shift isn’t just evolution—it’s a replatforming of the entire GTM function.

// Bio

Joel S. Horwitz has been riding the data wave since before it was cool—literally. He spoke at Spark Summit back in 2014 and penned a prescient piece for MIT Tech Review on data science and machine learning before they became boardroom buzzwords. A former big tech executive turned entrepreneur, Joel now runs Neoteric3D (N3D for short), a digital design and data growth agency that helps brands scale with smarts and style. When he’s not architecting next-gen growth strategies, you’ll find him logging long miles on the trail or coaching his sons’ soccer and baseball teams like a champ.

// Related Links

Website: https://www.neoteric3d.com

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Timestamps:

[00:00] Joel's preferred coffee

[00:53] Agentic workflows in marketing

[04:26] Agentic AI vs big data

[08:24] Creative outreach automation

[13:08] LLMs in marketing optimization

[17:36] Traffic relevance

[23:36] End-to-end AI workflow

[28:10] AI in task automation

[32:08] AI systems architecting

[38:00] AI vs Thought Leadership

[43:10] AI as sparring partner

[45:22] AI shifts human roles

[48:23] Wrap up

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// Abstract

Nikolaos' widely shared analysis on LinkedIn highlighted key insights across agentic AI, scaling laws, LLM development, and more. Now, he’s exploring how AI itself might be trained to automate this process in the future, offering a glimpse into how researchers could harness LLMs to synthesize conferences like NeurIPS in real-time.

// Bio

Nikolaos Vasiloglou is VP of Research-ML for RelationalAI, the industry's first knowledge graph coprocessor for the data cloud. Nikolaos has over 20 years of experience implementing high-value machine learning and AI solutions across various industries.

// Related Links

Website: https://relational.ai/

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Timestamps:

[00:00] Nik's preferred coffee

[01:05] Distilling NeurIPS insights

[06:43] Choosing research papers

[16:49] Agent patterns at NeurIPS

[21:16] Interest in agent-based innovation

[25:54] Time series forecasting models

[28:15] Tabular foundation models

[36:25] Verifier challenges and complexity

[39:36] Knowledge graph

[45:00] Knowledge graph data challenges

[47:14] Worldview in knowledge graphs

[50:30] Self-serve analytics challenges

[56:22] Llama model adaptation comparison

[56:59] Wrap up

AI Conversations Powered by Prosus Group 

Demetrios chats with Beyang Liu about Sourcegraph’s AMP, exploring how AI coding agents are reshaping development—from IDEs to natural language commands—boosting productivity, cutting costs, and redefining how developers work with code.

Guest speaker:

Beyang Liu - CTO of Sourcegraph

Host:

Demetrios Brinkmann - Founder of MLOps Community

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AI Conversations Powered by Prosus Group 

Stack Overflow is adapting to the AI era by licensing its trusted Q&A corpus, expanding into discussions and enterprise tools, and reinforcing its role as a reliable source as developer trust in AI output declines.

Guest speaker:

Prashanth Chandrashekar - CEO of Stack Overflow

Host:

Demetrios Brinkmann - Founder of MLOps Community

2025 Developer Survey: https://survey.stackoverflow.co/2025?utm_medium=referral&utm_source=direct-share&utm_campaign=dev-survey-2025&utm_content=MLOps

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