Feature Store Summit - Taming the beast: Building Scalable Features in the Wild at Prescient

This presentation is part of the Feature Store Summit.
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One of the most common hurdles with developing ML models is getting past all the data janitorial work to design informative features that can operate at scale. At Prescient, each client has their own distinct requirements and goals. We have to learn, develop, and maintain unique feature sets to match, limiting our ability to take on new projects and onboard new customers. Where do you start? What’s the best setup, and what’s involved in getting features/models to be production-ready? In this talk, you will learn how we approached these questions and ultimately landed on a feature store to help. With Rasgo, you can finally streamline feature engineering done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.