Huge thank you to Weights & Biases for sponsoring this episode. WandB Free Courses - http://wandb.me/courses_mlops

// Abstract

Uber's Michelangelo platform has evolved significantly through three major phases, enhancing its capabilities from basic ML predictions to sophisticated uses in deep learning and generative AI. Initially, Michelangelo 1.0 faced several challenges such as a lack of deep learning support and inadequate project tiering. To address these issues, Michelangelo 2.0 and subsequently 3.0 introduced improvements like support for Pytorch, enhanced model training, and integration of new technologies like Nvidia’s Triton and Kubernetes. The platform now includes advanced features such as a Genai gateway, robust compliance guardrails, and a system for monitoring model performance to streamline and secure AI operations at Uber.

// Bio

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios constantly learns and engages in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether analyzing the best paths forward, overcoming obstacles, or building Lego houses with his daughter.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey blog post: https://www.uber.com/en-JP/blog/from-predictive-to-generative-ai/

The Future of Feature Stores and Platforms // Mike Del Balso & Josh Wills // MLOps Podcast # 186: https://youtu.be/p5F7v-w4EN0

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Timestamps:

[00:00] Uber's Michelangelo platform evolution analyzed in podcast

[03:51 - 4:50] Weights & Biases Ad

[05:57] Uber creates Michelangelo to streamline machine learning

[07:44] Michelangelo platform's tech and flexible system

[11:49] Uber Michelangelo platform adapted for deep learning

[16:48] Uber invests in ML training for employees

[19:08] Explanation of blog content, ML quality metrics

[22:38] Michelangelo 2.0 prioritizes serving latency and Kubernetes

[26:30] GenAI gateway manages model routing and costs

[31:35] ML platform evolution, legacy systems, and maintenance

[33:22] Team debates maintaining outdated tools or moving on

[34:41] Please like, share, leave feedback, and subscribe to our MLOps channels!

[34:57] Wrap up