Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. ML models make predictions given a set of input data known as features, and data scientists easily spend more than 60% of their time designing and building these features. Furthermore, highly accurate predictions depend on timely access to feature values that change quickly over time, adding even more complexity to the job of building a highly available and accurate solution. For example, a model for a ride-sharing app can choose the best price for a ri­de from the airport, but only if it knows the number of ride requests received in the past 10 minutes and the number of passengers projected to land in the next 10 minutes. A routing model in a call center app can pick the best available

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