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Lorenzo Moriondo is a Technical Lead for AI at tuned.org.uk, working on AI agent protocols, graph-based search, and production-grade LLM systems.arrowspace: Vector Spaces and Graph Wiring // MLOps Podcast #365 with Lorenzo Moriondo, AI Research and Product EngineerJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// 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.// BioWith 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 LinksWebsite: https://www.tuned.org.uk~~~~~~~~ ✌️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 Chris on LinkedIn: /lorenzomoriondo

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.

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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

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