Agentic Conversations (formally mlops.community)

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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Connect with Kai on LinkedIn: /kai-wang-67457318/

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.

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// 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 Kostas on LinkedIn: /kostaspardalis/

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 about 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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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.

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// 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, which 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 a database, a 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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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

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 #324 with Sebastián Ramírez, Developer at FastAPI Labs.

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// 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 learn to solve, 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_page

https://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.

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// 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

Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI.

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// Abstract

AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations.

While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency. In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.

// Bio

Founder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in the early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development.

Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI.

// Related Links

Open source-based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform

Jukka's new company: https://8wave.ai

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Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Jukka on LinkedIn: /jukka-remes

Timestamps:

[00:00] Jukka's preferred coffee

[00:39] Open-Source Platform Benefits

[01:56] Silo MLOps Platform Explanation

[05:18] AI Model Production Processes

[10:42] AI Platform Use Cases

[16:54] Reproducibility in Research Models

[26:51] Pipeline setup automation

[33:26] MLOps Adoption Journey

[38:31] EU AI Act and Open Source

[41:38] MLOps and 8wave AI

[45:46] Optimizing Cross-Stakeholder Collaboration

[52:15] Open Source ML Platform

[55:06] Wrap up

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.

// Bio

Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google, where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.

// Related Links

Website: ⁠www.tecton.ai⁠

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Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠

MLOps Swag/Merch: [⁠https://shop.mlops.community/⁠]

Connect with Demetrios on LinkedIn: ⁠/dpbrinkm⁠

Connect with Mike on LinkedIn: ⁠/michaeldelbalso⁠

Timestamps:

[00:00] Smarter decisions, less manual work

[03:52] Data pipelines: pain and fixes

[08:45] Why Tecton was born

[11:30] ML use cases shift

[14:14] Models for big bets

[18:39] Build or buy drama

[20:20] Fintech's data playbook

[23:52] What really needs real-time

[28:07] Speeding up ML delivery

[32:09] Valuing ML is tricky

[35:29] Simplifying ML toolkits

[37:18] AI copilots in action

[42:13] AI that fights fraud

[45:07] Teaming up across coasts

[46:43] Tecton + Generative AI?