Browse Lessons

From the Legal Trenches to Tech // Nick Coleman // MLOps Podcast #332

Does your product require AI/ML

The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism

From Circuits to Street Smarts

Robots Are Learning—But Not Listening

A New Way of Building with AI

The Curious Case of LLM Benjamin

3 to 1,000 Use Cases

#search ai use case at @Uber #podcast #aiinpractice #mlops

Building AI that Doesn’t Break

Inside Uber’s AI Revolution - Everything about how they use AI/ML

Busting the Big AI Myths

Make Transformations Less Painful and More Powerful

What Different Kinds of Intelligence are There?

Data Teams Need SLAs

The Missing Data Stack for Physical AI

ML Engineers Who Ignore LLMs Are Voluntarily Retiring Early

#spark is over engineering it

unreliable #spark

You've got to have some metrics

Tools Shouldn't Feel Like a Puzzle

Auto Complete by Day, Batman by Night

Easy For Humans but Hard for AI

Not Every AI Agent Needs to Be Loud, Some Just Get the Job Done

Testing AI Intelligence: The Benchmarking Battle

How AI is Transforming Business Data Forever

This Is Your Sign to Build

Scaling AI with MLOps: The Silo AI Journey

Accuracy and the Right Amount of Fraud

The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // MLOps Podcast #324

Giving Prompt Ownership Makes Better AI Products?

Authentication: Is Your Agent Actually Speaking to Someone?

mcp vs agent to agent protocols

Everything Hard About Building AI Agents Today

Not Every Problem Needs a Human

How the 80s Help Make Sense of AI Today

Crafting Interfaces That Speak Human

Engineers Dreading Dropping in to debug a mess

Doing a Virtual Panel from an Uber

Cursor vs Devin UI/UX

Where do Evals Fit in?

MLOps Community Events are a Vibe

25,000 Lines of Code A Developer's Nightmare 📏💻

Optimizing LLMs through Prompt Engineering

Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // MLOps Podcast #322

RAG from Scratch with Best Practices (PART 2)

Going end-to-end

Real Talk: The Expectations of Machine Learning Products