Episode Details

Uber's Michelangelo platform forced the company to confront a problem most ML teams hit eventually: the same features being rebuilt repeatedly by different teams, with no shared infrastructure underneath. The decisions Uber made in 2017 and 2018 still define how serious organisations think about feature stores today.

Go deeper, free lessons

• Models in production: drift, monitoring & MLOps
• The AI operating model and platform
• Internal data platforms as products
• Batch vs streaming: choosing the right paradigm
• Closing the POC-to-production gap

Full transcript: https://www.mba-training.com/en/podcast/data/feature-store-ml-data-supply-chain-uber

The article: https://www.mba-training.com/en/blog/feature-store-ml-data-supply-chain-uber

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