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Kashish Mittal is a Staff Software Engineer at Uber, working on large-scale distributed systems and core backend infrastructure.Fixing GPU Starvation in Large-Scale Distributed Training // MLOps Podcast #367 with Kashish Mittal, Staff Software Engineer at Uber Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract Kashish zooms out to discuss a universal industry pattern: how infrastructure—specifically data loading—is almost always the hidden constraint for ML scaling.The conversation dives deep into a recent architectural war story. Kashish walks through the full-stack profiling and detective work required to solve a massive GPU starvation bottleneck. By redesigning the Petastorm caching layer to bypass CPU transformation walls and uncovering hidden distributed race conditions, his team boosted GPU utilization to 60%+ and cut training time by 80%. Kashish also shares his philosophy on the fundamental trade-offs between latency and efficiency in GPU serving.// BioKashish Mittal is a Staff Software Engineer at Uber, where he architects the hyperscale machine learning infrastructure that powers Uber’s core mobility and delivery marketplaces. Prior to Uber, Kashish spent nearly a decade at Google building highly scalable, low-latency distributed ML systems for flagship products, including YouTube Ads and Core Search Ranking. His engineering expertise lies at the intersection of distributed systems and AI—specifically focusing on large-scale data processing, eliminating critical I/O bottlenecks, and maximizing GPU efficiency for petabyte-scale training pipelines. When he isn't hunting down distributed race conditions, he is a passionate advocate for open-source architecture and building reproducible, high-throughput ML systems.// Related LinksWebsite: https://www.uber.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/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Kashish on LinkedIn: /kashishmittal/

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

Databricks Roundtable episode: Operationalizing AI Agents: From Experimentation to Production.

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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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Connect with Samraj on LinkedIn: /samrajmoorjani/
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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 Engineer

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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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Connect with Chris on LinkedIn: /lorenzomoriondo

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

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