Enterprise businesses often build separate, customized versions of vertical-specific models for each customer, requiring tight workflows and tooling to maintain velocity and quality. This talk describes the architecture and tools we built at EAB, an ed-tech company, to integrate many models predicting student graduation into an application. I'll provide guiding principles and how those led to use of a commercial API provider as well as a home-built DSL, a command-line workflow, and web apps for data and model validation.
Harlan D. Harris has a PhD in Computer Science/Machine Learning from the University of Illinois at Urbana-Champaign, and worked as a Cognitive Psychology researcher before turning to industry. He has worked at Kaplan Test Prep, the Advisory Board Company, WeWork and several startups in New York and DC. Harlan also co-founded the Data Science DC Meetup and Data Community DC, Inc., and co-wrote O'Reilly's Analyzing the Analyzers, a short e-book about the variety of data scientists.