Applied ML – How Uber Uses Machine Learning at Scale

This is one of the most comprehensive engineering blog posts on how Uber uses Machine Learning (ML) at scale. It covers:

  • Uber’s ML Platform – Michael Angelo
  • Uber’s research and production efforts and how they inter-relate
  • How Uber achieves Model Developers Velocity

I made a list of few terms and concepts from the article:

  • ML deployment use cases
  • Pervasive deployment of ML in several applications
  • Distributed training of ML
  • Aligning ML applications with Uber’s priorities
  • ML tools across the company (where and what)
  • Internal events like – ML conferences, ML reading groups, talk series
  • Data Science Workbench (a tool to build and iterate ML models)
  • ML Platform team and how they work to support ML development inside Uber
  • Technology stacks – Spark, Cassandra, Python and others
  • Experiments with external tools both open source and commercial
  • Uber’s open source contributions

It is nice to know how a dynamic company uses Machine Learning. There is a lot to learn from here. If you are thinking about building and deploying ML applications Scaling Machine Learning at Uber with Michelangelo | Uber Engineering Blog is a must read. I may go back and read it again.

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