Uber’s Michaelangelo — ML Platform

Michaelangelo — End to End ML Platform

Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across Uber. It is designed to cover the end-to-end ML workflow and it currently supports classical machine learning, time series forecasting, and deep learning models that span a myriad of use cases ranging from generating marketplace forecasts, responding to customer support tickets, to calculating accurate estimated times of arrival (ETAs) and powering Uber’s One-Click Chat feature using natural language processing (NLP) models on the driver app.

Why?

Around 2015, Uber’s ML engineers noticed the hidden technical debt in machine learning systems which we clarified in our technical debt series, or the ML equivalent of ‘But it works on my machine…’ . Engineers at Uber could built a custom, one-off systems that integrated with ML models, but they added to the technical debt and were not scalable in a large engineering organization. In their own words,

There were no systems in place to build reliable, uniform, and reproducible pipelines for creating and managing training and prediction data at scale.

That’s why they built Michelangelo. It relies on Uber’s data lake of transactional and logged…