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Michelangelo is the machine learning (ML) platform of Uber. It enables users across the company to build, deploy and operate machine learning solutions at scale. It is designed to cover the whole machine learning workflow, manage data, train, evaluate and deploy models, and make and monitor predictions.
Michelangelo is the internal ML-as-a-service platform that democratizes machine learning and uses scaling artificial intelligence to meet the needs of businesses. It is created to be the system that addresses scalable model training and dispatch to production serving vessels.
The Uber Engineering team started the development of Michelangelo in mid-2015. It was built with a mix of open-source systems and components made in-house. The primary open-sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
Michelangelo can be used primarily for automating different aspects of the lifecycle of ML models, which expedites the process for engineering teams. It can also be used to streamline and manage workflows for the teams.
Subsidiaries of Uber, such as Uber Eats, use Michelangelo as its machine learning platform. Michelangelo can be used in a variety of contexts:
- generating marketplace forecasts
- acting as customer service to respond to customer support tickets
- calculating accurate estimated times of arrival for transportation
- powering one-click chat features on apps.
