Demetrios Brinkmann, AI strategist at Uber, discusses the evolution of Michelangelo platform at Uber, from basic ML predictions to deep learning and generative AI. Covering challenges faced in early versions and improvements in Michelangelo 2.0 and 3.0 like Pytorch support, enhanced model training, and integration of technologies like Nvidia’s Triton and Kubernetes. The platform now includes features like a Genai gateway, compliance guardrails, and model performance monitoring to streamline AI operations.
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Quick takeaways
Uber's Michelangelo platform evolved from basic ML to deep learning and generative AI, enhancing AI operations at Uber.
Michelangelo 2.0 and 3.0 introduced support for PyTorch, model training enhancements, and integration of technologies like Nvidia's Triton and Kubernetes.
Deep dives
Evolution of Michelangelo Platform at Uber
Uber's Michelangelo platform evolved through three distinct phases, starting from foundational predictive ML and tabular data, progressing to deep learning, and eventually venturing into generative AI. The platform supported various use cases like Riot ETA, fraud detection, search, and driver matching, enhancing the real-time Uber product experience.
Challenges and Innovations in Michelangelo 1.0
In the initial phase (2016-2019), challenges in machine learning at Uber led to the creation of the Michelangelo platform, moving away from ad hoc ML approaches. The platform integrated tools like TensorFlow, Spark, XGBoost, and introduced the concept of a feature store, inspiring tech adoption outside Uber.
Transition to Michelangelo 2.0 and Deep Learning Emphasis
With Michelangelo 2.0 (2019-2023), Uber focused on enhancing ML impact, allowing advanced techniques like deep learning. The platform's design principles prioritized flexibility, project tiering, and self-service. Shifts to PyTorch, PyTorch Lightning, and Ray reflected openness to external tools, while emphasizing deep learning as a core offering.
Uber's Michelangelo: Strategic AI Overhaul and Impact // MLOps podcast #239 with Demetrios Brinkmann.
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/
Machine Learning Education at Uber // Melissa Barr & Michael Mui // MLOps Podcast #156: https://youtu.be/N6EbBUFVfO8
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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
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