Michelangelo - Machine Learning @Uber
QCon San Francisco, the international software conference, returns November 17-21, 2025. Join senior software practitioners from early adopter companies as they share real-world insights and actionable advice to help you adopt the right technologies and practices. Gain exposure to innovative approaches and fresh ideas in software development and engineering, designed to inspire and challenge you. Don’t miss this chance to deepen your knowledge, sharpen your skills, and stay ahead in the ever-evolving world of technology. Register now: https://bit.ly/3BYzfbe ------------------------------------------------------------------------------------------------------------------------------------------ Video with transcript included: http://bit.ly/2Psd9B6 Jeremy Hermann talks about Michelangelo - the ML Platform that powers most of the ML solutions at Uber. The early goal was to enable teams to deploy and operate ML solutions at Uber scale. Now, their focus has shifted towards developer velocity and empowering the individual model owners to be fully self-sufficient from early prototyping through full production deployment & operationalization. This presentation was recorded at QCon San Francisco 2018: https://bit.ly/2uYyHLb The next QCon is Qcon New York 2019 – June 24-26, 2019: http://bit.ly/2Uyj39C For more awesome presentations on innovator and early adopter topics check InfoQ’s selection of talks from conferences worldwide https://bit.ly/2tm9loz Interested in Artificial Intelligence, Machine Learning and Data Engineering? Follow the topic on InfoQ: https://bit.ly/2rrEicK #MachineLearning #Uber #CaseStudy #InfoQ #QConSanFrancisco

Michelangelo: Uber's machine learning platform - Achal Shah

Scaling Patterns for Netflix's Edge

Machine Learning Model Deployment: Strategy to Implementation

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

Mastering Chaos - A Netflix Guide to Microservices

The Whys and Hows of Database Streaming

ML System Design: Feature Store

Feast: feature store for Machine Learning

Scaling Pinterest • Marty Weiner • GOTO 2014

Engineering Systems for Real-Time Predictions @DoorDash

Practical Change Data Streaming Use Cases with Apache Kafka & Debezium

Serving a Billion Personalized News Feeds

Bighead: Airbnb's end-to-end Machine Learning Platform | Airbnb

Accelerating the ML Lifecycle with an Enterprise-Grade Feature Store

Something is jamming GPS over Europe. Here's what we found

Machine Learning & the Uber Marketplace

I turned an old van into a 2-STORY tiny house

Future of Data Engineering

The Anatomy of a Distributed System

