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Donné Stevenson is a Machine Learning Engineer at Prosus, working on scalable ML infrastructure and productionizing GenAI systems across portfolio companies.Pedro Chaves is a Data Science Manager at OLX Group, working on GenAI-powered search, personalization, and large-scale marketplace recommendations.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractMarketplaces 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?// BioDonné StevensonFocused on building AI-powered products that give companies the tools and expertise needed to harness the power of AI in their respective fields.
Pedro ChavesPedro 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 LinksWebsite: https://www.prosus.com/Website: https://www.olxgroup.com/~~~~~~~~ ✌️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/]MLOps GPU Guide: https://go.mlops.community/gpuguide

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

Johann Schleier-Smith is the Technical Lead for AI at Temporal Technologies, working on reliable infrastructure for production AI systems and long-running agent workflows.

Durable Execution and Modern Distributed Systems, Johann Schleier-Smith // MLOps Podcast #364

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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
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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

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
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Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Johann on LinkedIn: /jssmith/

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

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
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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/]

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/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
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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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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 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