Stop Making Data Scientists Do Systems

Presented by

Emily Curtin, Senior Machine Learning Engineer, Mailchimp

About this talk

Data Scientists aren’t Systems Engineers, so why do our tools expect them to understand arcane k8s errors? Why do our people systems effectively model them as weird web developers? Many organizations are lacking in a practical understanding of the Data Scientist persona from a UX perspective. By defining what Data Scientists are good at, and more importantly what they’re not good at, we as MLOps professionals and organizational leaders can build on that understanding and let Data Scientists do their best work. 3 Key Takeaways - The best tools for Data Scientists are low/no-systems, not low/no-code. - Velocity comes from good tooling; quality comes from good incentives. - Infrastructure abstraction should be a top priority for MLOps professionals."

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