AI Conversations Powered by Prosus Group Stack Overflow is adapting to the AI era by licensing its trusted Q&A corpus, expanding into discussions and enterprise tools, and reinforcing its role as a reliable source as developer trust in AI output declines.Guest speaker:Prashanth Chandrashekar - CEO of Stack OverflowHost:Demetrios Brinkmann - Founder of MLOps Community2025 Developer Survey: https://survey.stackoverflow.co/2025?utm_medium=referral&utm_source=direct-share&utm_campaign=dev-survey-2025&utm_content=MLOps~~~~~~~~ ✌️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/]
Knowledge is Eventually Consistent // MLOps Podcast #335 with Devin Stein, CEO of Dosu.
Grateful to @Databricks and @hyperbolic-labs for supporting our podcast and helping us keep great conversations going.
Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter
// Abstract
AI as a partner in building richer, more accessible written knowledge—so communities and teams can thrive, endure, and expand their reach.
// Bio
Devin is the CEO and Founder of Dosu. Prior to Dosu, Devin was an early engineer and leader at various startups. Outside of work, he is an active open source contributor and maintainer.
// Related Links
Website: https://github.com/devstein
https://www.youtube.com/watch?v=sC8aW47DqPg
https://www.youtube.com/watch?v=PuM0Gd3txfQ
https://www.youtube.com/watch?v=ah6diDQ9wyw
https://www.youtube.com/watch?v=x22FEQic8lg
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Timestamps:
[00:00] Devin's preferred coffee
[00:53] Facts agent overview
[03:47] Decision state detection
[07:55 - 8:41] Databricks ad
[08:42] Context-dependent word meanings
[15:25] Fact lifecycle management
[24:40] Maintaining quality documentation
[30:10 - 31:06] Hyperbolic ad
[31:07] Agent collaboration scenarios
[38:22] Knowledge maintenance
[44:10] Deployment and integration strategies
[48:13] Flywheel data approach
[51:54] Horror story engineering function
[54:32] Wrap up
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 reflects 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 Process 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 GroupBruce 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 GroupDmitri 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.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// 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 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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Connect with Sherwood on LinkedIn: /sherwoodcallaway/
#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.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// 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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Connect with Nick on LinkedIn: /nicklcoleman/
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 $1.1B 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 Master 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
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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
Kai Wang joins the MLOps Community podcast LIVE to share how Uber built and scaled its ML platform, Michelangelo. From mission-critical models to tools for both beginners and experts, he walks us through Uber’s AI playbook—and teases plans to open-source parts of it.
// Bio
Kai Wang is the product lead of the AI platform team at Uber, overseeing Uber's internal end-to-end ML platform called Michelangelo that powers 100% Uber's business-critical ML use cases.
// Related Links
Uber GenAI: https://www.uber.com/blog/from-predictive-to-generative-ai/
#uber #podcast #ai #machinelearning
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Timestamps:
[00:00] Rethinking AI Beyond ChatGPT
[04:01] How Devs Pick Their Tools
[08:25] Measuring Dev Speed Smartly
[10:14] Predictive Models at Uber
[13:11] When ML Strategy Shifts
[15:56] Smarter Uber Eats with AI
[19:29] Summarizing Feedback with ML
[23:27] GenAI That Users Notice
[27:19] Inference at Scale: Michelangelo
[32:26] Building Uber’s AI Studio
[33:50] Faster AI Agents, Less Pain
[39:21] Evaluating Models at Uber
[42:22] Why Uber Open-Sourced Machanjo
[44:32] What Fuels Uber’s AI Team
The Missing Data Stack for Physical AI // MLOps Podcast #328 with Nikolaus West, CEO of Rerun.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// Abstract
Nikolaus West, CEO of Rerun, breaks down the challenges and opportunities of physical AI—AI that interacts with the real world. He explains why traditional software falls short in dynamic environments and how visualization, adaptability, and better tooling are key to making robotics and spatial computing more practical.
// Bio
Niko is a second-time founder and software engineer with a computer vision background from Stanford. He’s a fanatic about bringing great computer vision and robotics products to the physical world.
// Related Links
Website: rerun.io
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Timestamps:
[00:00] Niko's preferred coffee
[00:35] Physical AI vs Robotics Debate
[04:40] IoT Hype vs Reality
[12:16] Physical AI Lifecycle Overview
[20:05] AI Constraints in Robotics
[23:42] Data Challenges in Robotics
[33:37] Open Sourcing AI Tools
[39:36] Rerun Platform Integration
[40:57] Data Integration for Insights
[45:02] Data Pipelines and Quality
[49:19] Robotics Design Trade-offs
[52:25] Wrap up
LLMs are reshaping the future of data and AI—and ignoring them might just be career malpractice. Yoni Michael and Kostas Pardalis unpack what’s breaking, what’s emerging, and why inference is becoming the new heartbeat of the data pipeline.
// Bio
Kostas Pardalis
Kostas is an engineer-turned-entrepreneur with a passion for building products and companies in the data space. He’s currently the co-founder of Typedef. Before that, he worked closely with the creators of Trino at Starburst Data on some exciting projects. Earlier in his career, he was part of the leadership team at Rudderstack, helping the company grow from zero to a successful Series B in under two years. He also founded Blendo in 2014, one of the first cloud-based ELT solutions.
Yoni Michael
Yoni is the Co-Founder of typedef, a serverless data platform purpose-built to help teams process unstructured text and run LLM inference pipelines at scale. With a deep background in data infrastructure, Yoni has spent over a decade building systems at the intersection of data and AI — including leading infrastructure at Tecton and engineering teams at Salesforce.
Yoni is passionate about rethinking how teams extract insight from massive troves of text, transcripts, and documents — and believes the future of analytics depends on bridging traditional data pipelines with modern AI workflows. At Typedef, he’s working to make that future accessible to every team, without the complexity of managing infrastructure.
// Related Links
Website: https://www.typedef.ai
https://techontherocks.show
https://www.cpard.xyz
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Connect with Yoni on LinkedIn: /yonimichael/
Timestamps:
[00:00] Breaking Tools, Evolving Data Workloads
[06:35] Building Truly Great Data Teams
[10:49] Making Data Platforms Actually Useful
[18:54] Scaling AI with Native Integration
[24:04] Empowering Employees to Build Agents
[28:17] Rise of the AI Sherpa
[36:09] Real AI Infrastructure Pain Points
[38:05] Fixing Gaps Between Data, AI
[46:04] Smarter Decisions Through Better Data
[50:18] LLMs as Human-Machine Interfaces
[53:40] Why Summarization Still Falls Short
[01:01:15] Smarter Chunking, Fixing Text Issues
[01:09:08] Evaluating AI with Canary Pipelines
[01:11:46] Finding Use Cases That Matter
[01:17:38] Cutting Costs, Keeping AI Quality
[01:25:15] Aligning MLOps to Business Outcomes
[01:29:44] Communities Thrive on Cross-Pollination
[01:34:56] Evaluation Tools Quietly Consolidating
What makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical.
// Bio
Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications.By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers.Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools.He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company.
// Related Links
Website: https://gregkamradt.com/
YouTube channel: https://www.youtube.com/@DataIndependent
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Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Greg on LinkedIn: /gregkamradt/
Timestamps:
[00:00] Human-Easy, AI-Hard
[05:25] When the Model Shocks Everyone
[06:39] “Let’s Circle Back on That Benchmark…”
[09:50] Want Better AI? Pay the Compute Bill
[14:10] Can We Define Intelligence by How Fast You Learn?
[16:42] Still Waiting on That Algorithmic Breakthrough
[20:00] LangChain Was Just the Beginning
[24:23] Start With Humans, End With AGI
[29:01] What If Reality’s Just... What It Seems?
[32:21] AI Needs Fewer Vibes, More Predictions
[36:02] Defining Intelligence (No Pressure)
[36:41] AI Building AI? Yep, We're Going There
[40:13] Open Source vs. Prize Money Drama
[43:05] Architecting the ARC Challenge
[46:38] Agent 57 and the Atari Gauntlet
Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// Abstract
I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.
// Bio
Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.
As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.
// Related Links
Website: https://www.snowleopard.ai/
AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ
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Connect with Deepti on LinkedIn: /thedeepti/
Timestamps:
[00:00] Deepti's preferred coffee
[00:49] MLflow vs Kubeflow Debate
[04:58] GenAI Data Integration Challenges
[09:02] GenAI Sidecar Spicy Takes
[14:07] Troubleshooting LLM Hallucinations
[19:03] AI Overengineering and Hype
[25:06] Self-Serve Analytics Governance
[33:29] Dashboards vs Data Quality
[37:06] Agent Database Context Control
[43:00] LLM as Orchestrator
[47:34] Tool Call Ownership Clarification
[51:45] MCP Server Challenges
[56:52] Wrap up
The Creator of FastAPI’s Next Chapter // MLOps Podcast #322 with Sebastián Ramírez, Developer at FastAPI Labs.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// Abstract
The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs.
// Bio
Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open source tools.He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem.
// Related Links
Website: https://tiangolo.com/
FastAPI: https://fastapi.tiangolo.com/
FastAPI Cloud: https://fastapicloud.com/
FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Tiangolo on LinkedIn: /tiangolo
Timestamps:
[00:00] Sebastián's preferred coffee
[00:15] Takeaways
[01:43] Why Pydantic is Awesome
[06:47] ML Background and FastAPI
[10:44] NASA FastAPI Emojis
[15:21] FastAPI Cloud Journey
[26:07] FastAPI Cloud Open-Source Balance
[31:45] Basecamp Design Philosophy
[35:30] AI Abstraction Strategies
[42:56] Engineering vs Developer Experience
[51:40] Dogfooding and Docs Strategy
[59:44] Code Simplicity and Trust
[1:04:26] Scaling Without Losing Vision
[1:08:20] FastAPI Cloud Signup
[1:09:23] Wrap up
Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance.
The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success.
Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing.
Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve.
// Bio
Shreya Shankar
PhD student in data management for machine learning.
Willem Pienaar
Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories.
Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high scale ML systems for the Southeast Asian decacorn.
// Related Links
https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_pagehttps://cleric.ai/
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Shreya on LinkedIn: /shrshnk
Connect with Willem on LinkedIn: /willempienaar
Timestamps:
[00:00] Trust Issues in AI Data
[04:49] Cloud Clarity Meets Retrieval
[09:37] Why Fast AI Is Hard
[11:10] Fixing AI Communication Gaps
[14:53] Smarter Feedback for Prompts
[19:23] Creativity Through Data Exploration
[23:46] Helping Engineers Solve Faster
[26:03] The Three Gaps in AI
[28:08] Alerts Without the Noise
[33:22] Custom vs General AI
[34:14] Sharpening Agent Skills
[40:01] Catching Repeat Failures
[43:38] Rise of Self-Healing Software
[44:12] The Chaos of Monitoring AI
Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks. Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract
Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.
// Bio
Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.
Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.
// Related Links
Website: https://www.databricks.com/
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Raj on LinkedIn: /rajammanabrolu
Timestamps:
[00:00] Raj's preferred coffee
[00:36] Takeaways
[01:02] Tao Naming Decision
[04:19] No Labels Machine Learning
[08:09] Tao and TAO breakdown
[13:20] Reward Model Fine-Tuning
[18:15] Training vs Inference Compute
[22:32] Retraining and Model Drift
[29:06] Prompt Tuning vs Fine-Tuning
[34:32] Small Model Optimization Strategies
[37:10] Small Model Potential
[43:08] Fine-tuning Model Differences
[46:02] Mistral Model Freedom
[53:46] Wrap up
