Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations.

// Bio

Arpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding.

She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita’s work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community.

Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI is reflected both in her academic pursuits and professional contributions.

// Related Links

#recommendersystems #LLMs #linkedin

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Timestamps:

[00:00] Smarter Content Recommendations

[05:19] LLMs: Next-Gen Recommendations

[09:37] Judging LLM Suggestions

[11:38] Old vs New Recommenders

[14:11] Why LLMs Get Stuck

[16:52] When Old Models Win

[22:39] After-Booking Rec Magic

[23:26] One LLM to Rule Models

[29:14] Personalization That Evolves

[32:39] SIM Beats Transformers in QA

[35:35] Agents Writing Research Papers

[37:12] Big-Company Agent Failures

[41:47] LinkedIn Posts Fade Faster

[46:04] Clustering Shifts Social Feeds

[47:01] Vanishing Posts, Replay Mode

Agents in Production [Podcast Limited Series] Episode Nine – Training LLMs, Picking the Right Models, and GPU Headaches

Paul van der Boor and Zulkuf Genc from Prosus join Demetrios to talk about what it really takes to get AI agents running in production. From building solid eval sets to juggling GPU logistics and figuring out which models are worth using (and when), they share hard-won lessons from the front lines. If you're working with LLMs at scale—or thinking about it—this one’s for you.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Zulkuf Genc - Director of AI at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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Demetrios chats with Paul van der Boor and Bruce Martens from Prosus about the real bottlenecks in AI agent development—not tools, but evaluation and feedback. They unpack when to build vs. buy, the tradeoffs of external vendors, and how internal tools like Copilot are reshaping workflows.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Bruce Martens - AI Engineer at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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Building AI agents that actually get things done is harder than it looks. Demetrios, Paul, and Dmitri break down what makes agents effective—from smart planning and memory to treating tools, systems, and even people as components. They cover the "react" loop, budgeting for long tasks, sandboxing, and learning from experience. It’s a sharp, practical look at what it really takes to design useful, adaptive AI agents.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Dmitri Jarnikov - Senior Director of Data Science at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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Building AI Agents that work is no small feat.

In Agents in Production [Podcast Limited Series] - Episode Six, Paul van der Boor and Sean Kenny share how they scaled AI across 100+ companies with Toqan—a tool born from a Slack experiment and grown into a powerful productivity platform. From driving adoption and building super users to envisioning AI employees of the future, this conversation cuts through the hype and gets into what it really takes to make AI work in the enterprise.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Sean Kenny - Senior Product Manager at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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Real-time Feature Generation at Lyft // MLOps Podcast #334 with Rakesh Kumar, Senior Staff Software Engineer at Lyft.

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// Abstract

This session delves into real-time feature generation at Lyft. Real-time feature generation is critical for Lyft where accurate up-to-the-minute marketplace data is paramount for optimal operational efficiency. We will explore how the infrastructure handles the immense challenge of processing tens of millions of events per minute to generate features that truly reflect current marketplace conditions.

Lyft has built this massive infrastructure over time, evolving from a humble start and a naive pipeline. Through lessons learned and iterative improvements, Lyft has made several trade-offs to achieve low-latency, real-time feature delivery. MLOps plays a critical role in managing the lifecycle of these real-time feature pipelines, including monitoring and deployment. We will discuss the practicalities of building and maintaining high-throughput, low-latency real-time feature generation systems that power Lyft’s dynamic marketplace and business-critical products.

// Bio

Rakesh Kumar is a Senior Staff Software Engineer at Lyft, specializing in building and scaling Machine Learning platforms. Rakesh has expertise in MLOps, including real-time feature generation, experimentation platforms, and deploying ML models at scale. He is passionate about sharing his knowledge and fostering a culture of innovation. This is evident in his contributions to the tech community through blog posts, conference presentations, and reviewing technical publications.

// Related Links

Website: https://englife101.io/

https://eng.lyft.com/search?q=rakesh

https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24

https://eng.lyft.com/evolution-of-streaming-pipelines-in-lyfts-marketplace-74295eaf1eba

Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // MLOps Podcast #302 - https://youtu.be/0axFbQwHEh8

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Timestamps:

[00:00] Rakesh preferred coffee

[00:24] Real-time machine learning

[04:51] Latency tricks explanation

[09:28] Real-time problem evolution

[15:51] Config management complexity

[18:57] Data contract implementation

[23:36] Feature store

[28:23] Offline vs online workflows

[31:02] Decision-making in tech shifts

[36:54] Cost evaluation frequency

[40:48] Model feature discussion

[49:09] Hot shard tricks

[55:05] Pipeline feature bundling

[57:38] Wrap up

Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks.

He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals.

Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify?

// Bio

Sherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs.

Alice is an advanced agentic AI working in production and at scale. Under Sherwood’s leadership, the system grew from an initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR.

Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex’s application and engineering org through hypergrowth. He currently lives in San Francisco, CA.

// Related Links

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#aiengineering

Timestamps:

[00:00] AI Takes Over Health Calls

[05:05] What Can Agents Really Do?

[08:25] Who’s in Charge—User or Agent?

[11:20] Why Graphs Matter in Agents

[15:03] How Complex Should Agents Be?

[18:33] The Hidden Cost of Model Upgrades

[21:57] Inside the LLM Agent Loop

[25:08] Turning Agents into APIs

[29:06] Scaling Agents Without Meltdowns

[30:04] The Monorepo Tangle, Explained

[34:01] Building Agents the Open Source Way

[38:49] What Production-Ready Agents Look Like

[41:23] AI That Fixes Code on Its Own

[43:26] Tracking Agent Behavior with OpenTelemetry

[46:43] Running Agents Locally with Phoenix

[52:55] LangGraph Meets Arise for Agent Control

[53:29] Hunting Hallucinations in Agent Traces

[56:45] Off-Script Insights Worth Hearing

From the Legal Trenches to Tech // MLOps Podcast #332 with Nick Coleman, Attorney/Founder of LexMed.

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// Abstract

Nick Coleman shares his journey from high-volume Social Security disability practice to founding LexMed, a legal tech startup leveraging AI to transform how attorneys handle complex cases. He'll discuss LexMed's dual AI platforms: Hearing Echo, which automates transcription and analysis of disability hearings with speaker identification and critical testimony validation, and ChartVision, which combines human medical abstraction with AI to extract and map medical evidence to disability criteria. Nick will explain how "vibe coding" has dramatically reduced friction between his subject matter expertise and technical implementation, enabling rapid prototyping that preserves legal insights through development. By bridging domain knowledge and technology, LexMed has created solutions that address the real-world challenges he experienced firsthand in his high-volume disability practice, offering valuable lessons for AI implementation in other specialized fields.

// Bio

Nick Coleman is the founder and CEO of LexMed, a legal tech startup applying advanced AI to transform the practice of law. As a Social Security disability attorney with extensive appellate experience, Nick identified critical inefficiencies in legal workflows that technology could solve. LexMed's flagship product, Hearing Echo, leverages speech recognition and natural language processing to automate the transcription and analysis of disability hearing audio, dramatically improving case management for attorneys. Nick holds an AV Preeminent rating from Martindale-Hubbell, has been recognized as a Super Lawyers Rising Star, and serves on the Arkansas Bar Artificial Intelligence Task Force. With deep expertise at the intersection of law and technology, Nick is passionate about democratizing access to justice through innovative AI solutions.

// Related Links

Website: www.lexmed.ai

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Timestamps:

[00:00] Disability Claims Advocacy

[00:29] AI Native Startup

[02:08] Disability Claims Process

[07:56] Tech Journey

[10:52] AI in Document Review

[13:57] Building a Case for Appeal

[19:26] Medical Claims Language Model

[23:37] Tech-Driven Compliance Solutions

[30:31] Claim Prioritization Strategy

[34:57] Wrap up

The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // MLOps Podcast #331 with Frank Meehan, Founder and CEO of Frontier One AI.

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

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

// Abstract

“The awakening of every single country is that they have to control their AI intelligence and not outsource their data" - Jensen Huang. Sovereign AI is rapidly becoming a fundamental national utility, much like defense, energy, or telecoms.

Nations worldwide recognize that AI sovereignty—having control over your AI infrastructure, data, and models—is essential for economic progress, security, and especially independence, especially when the US is pushing protectionism and trying to prevent global AI innovation. Of course, this has the opposite effect - DeepSeek, created by a Hedge Fund in China; India building the world's largest AI data centre (3 GW), and global software teams scaling, learning, and building faster than ever before.

However, most countries lack the talent, financing, and experience to implement Sovereign AI for their requirements - and it is our belief at Frontier One that one of the biggest markets for AI applications, cloud services, and GPUs will be global governments. We see it already - with $10B of GPUs in 2024 bought directly by governments - and it's rapidly expanding. We will talk about what Sovereign AI is - both infrastructure and software details / why it is crucial for a nation / how to get involved as part of the MLOps community.

// Bio

Co-Founder of Frontier One - building Sovereign AI Factories and Cloud software for global markets.

Frank is a 2X CEO | 2X CMO (with 2X exits + 1 IPO NYSE), Board Director (Spotify, Siri), and Investor (SparkLabs Group) with 20+ years of experience in creating and growing leading brands, products, and companies.

Chair of Improvability, automating due diligence and reporting for corporates, foundations, and Governments with AI.

Co-founder and partner at SparkLabs Group - investors in OpenAI, Anthropic, 88 Rising, Discord, Animoca, Andela, Vectara, Kneron, Messari, Lifesum + 400 companies in our portfolio. Investment Committee and LP at SparkLabs Cultiv8 with 56 investments in consumer food and regenerative agriculture companies.

Co-founder and CMO - later CEO - of Equilibrium AI (Singapore), building it to one of the leading ESG and Carbon data management platforms globally. Equilibrium was acquired by FiscalNote in 2021, where he joined the senior leadership team, running the ESG business globally, and helping the company IPO in 2022 on the NYSE at $1.1 B valuation.

Board director at Spotify (2009-2012); Siri (2009-2010 exited to Apple); Lifesum (leading AI health app with 50 million users), seed investor in 88 Rising (Asia’s leading independent music label); CEO/CMO and co-founder at INQ Mobile (mobile internet pioneer); and Global Director for devices and products at 3 Mobile.

Started as a software developer with Ericsson Mobile in Sweden, after graduating from KTH in Stockholm and the University of Sydney with a Bachelor of Mechanical Engineering and a Master's of Science in Fluid Mechanics.

// Related Links

https://www.frontierone.ai/ and

https://www.sparklabsgroup.com

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Thanks to MLflow for supporting this episode — the platform helping teams track, manage, and deploy ML and GenAI projects with ease. Try it free at mlflow.org.

What if AI could build and maintain your software—like a co-worker who never forgets state? In this episode, Jiquan Ngiam chats with Demetrios about agents that actually do the work: parsing emails, updating spreadsheets, and reshaping how we design software itself. Less hype, more hands-on AI—tune in for a glimpse at the future of truly personalized computing.

// Bio

Jiquan Ngiam is the Co-Founder and CEO of Lutra AI, with deep expertise in artificial intelligence and machine learning. He was previously at Google Brain, Coursera, and in the Stanford CS Ph.D. program advised by Andrew Ng. He helped develop the first online courses in Machine Learning, and is now building agentic AI systems that can complete tasks for us.

// Related Links

https://www.youtube.com/@LutraAI

#api #llm #lutra #costefficiency #latentspace

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Timestamps:

[00:00] Agents That Actually Do Work

[08:21] Building Tables With AI Help

[12:54] Guardrails for Smarter Code

[16:35 - 18:00] MLFlow Ad[18:30] What’s Next for MCP?

[23:23] AI as Your Data Conductor

[31:13] Rethinking AI + Data Stacks

[32:10] Sandbox Security, Real Risks

[40:48] Smarter Reviews, Powered by Use

[46:08] Cost vs. Quality in AI

[52:00] Podcast Editing Gets Creative

[56:27] Transparent UIs, Powered by AI

[01:00:28] Can AI Learn Good Taste?

[01:04:45] Peeking Into Wild AI Futures