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Using CockroachDB to Reduce Feature Store Costs by 75%

While building a feature store to handle the massive growth of our machine-learning (“ML”) platform, we learned that using a mix of different databases can yield significant gains in efficiency and operational simplicity.

How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

At DoorDash, we make millions of predictions every second to power machine learning applications to enhance our search, recommendation, logistics, and fraud areas,  and scaling these complex systems along with our feature store is continually a challenge.

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints.