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Productionizing ML models is a significant challenge with only half of models making it into production. When developing ML models, ML teams spend most of their time engineering features, acquiring and cleaning data, and transforming it to form features.
A modern data infrastructure can address these challenges, and feature stores are a promising addition to the ML technology stack with the potential to increase the efficiency of model development and production.
This session introduces the concept of the feature store and provides examples of companies (AT&T, DoorDash, Zomato, and others) that have built feature stores using a variety of technologies including Redis.
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