Creating and operating ML models from event-based data using feature stores and feature engines

Authoring features respecting the constraints of time is hard, but required, when computing from event-based data directly. We’ll review the limitations of data science tools and deep dive on how we’ve solved the problem. Attendees will gain understanding of temporal processing to create and operate predictive models with event-based data. Feature engineering is supposed to be an iterative process, transforming raw data into training examples and feature vectors. Iteration is key -- but, each cycle should include trying new ideas offline, as well as testing in production. Offline experimentation requires historical event-based data to compute training examples at the right points-in-time—quickly, without waiting for complex pipelines to be built just to determine if a feature will be useful. Then, in the latter part of each iteration cycle, we need to test the new model live—without worrying about offline and online discrepancies. Feature stores are the newest idea that is supposed to help us, but it turns out that’s not enough. In this session, you’ll learn how to craft production-ready features and build training datasets at the right points-in-time from event-based data. Specifically, we’ll be covering strategies for powering feature stores with a feature engine to: - Compute directly from event-based data to try new features - Iterate on feature definitions and time selection across historical data instantly - Join values between different entities at precise times — without leakage - Eliminate data discrepancies in production Come join us to learn how to finally iterate on amazing ML models with event-based data.