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March 3rd, Computer History Museum CODING AGENTS CONFERENCE, come join us while there are still tickets left.
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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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Chris on LinkedIn: /cfregly

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

Roundtable CAST AI episode: Serving LLMs in Production: Performance, Cost & Scale.

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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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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Ioana on LinkedIn: /ioanaapetrei/
Connect with Igor on LinkedIn: /igor-%C5%A1u%C5%A1i%C4%87/

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

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Rahul on LinkedIn: /rahulraja963/

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

~~~~~~~~ ✌️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)]
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MLOps Swag/Merch: [https://shop.mlops.community/]
MLOps GPU Guide: https://go.mlops.community/gpuguide

Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Vincent on LinkedIn: /vincentwarmerdam/

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

~~~~~~~~ ✌️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 Ereli on LinkedIn: /erelieran/

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