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How Robinhood Built a Feature Store Using Feast

Posted Jul 14
# Feature Stores
# Open Source
# Production Use Case
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SPEAKER
Yuyang (Rand) Xie
Yuyang (Rand) Xie
Yuyang (Rand) Xie
Senior Machine Learning Engineer @ Robinhood

Rand is a Senior Machine Learning Engineer at Robinhood, focusing on ML serving stack including model serving and feature store. Before Robinhood, Rand worked at Google and was one of the founding engineers of Vertex AI's Feature Store product.

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Rand is a Senior Machine Learning Engineer at Robinhood, focusing on ML serving stack including model serving and feature store. Before Robinhood, Rand worked at Google and was one of the founding engineers of Vertex AI's Feature Store product.

+ Read More
SUMMARY

Features are essential to ML models. Therefore, a good feature infrastructure is important to any organization that wants to use ML properly in production. Feast is a great tool for building up your feature infrastructure. However, using Feast in production may need customization of your tech stack, extension for advanced use cases, and improvement of reliability and observability. In this talk, we will share the lessons learned from how Robinhood built a feature store from Feast.

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