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Engineering AI-Enabled Systems with Interdisciplinary Teams
Traditional machine learning and data science education focuses on building models and evaluating model accuracy on fixed datasets. In practice though, these models are often just one (albeit important) component in a larger system that need to be designed, developed, deployed, updated, and maintained. Operating such AI-enabled system requires attention to cost, operations, system design, error handling, maintainability, and many other engineering concerns traditionally in the realm of software engineers. In this talk, I explore how data scientists and software engineers have different but complementary roles in building AI-enabled systems, how shifting to a systems perspective enables a larger view, how software engineering methods, such as system and tradeoff thinking, hazard analysis, and requirements analysis are more important than ever, and how DevOps may provide an analogy about how to think about collaborating in interdisciplinary teams and how to educate the next generation of software engineers and data scientists.

Jun 18, 2020 12:00 PM in Montreal

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