Senior Machine Learning Engineer, Mailchimp
Emily May Curtin is a Senior Machine Learning Engineer at Mailchimp, which is definitely what she thought she’d be doing back when she went to film school. She combines her wealth of experience in DevOps, data engineering, distributed systems, and “cloud stuff” to enable Data Scientists at Mailchimp to do their best work. Truthfully, she’d rather be at her easel painting hurricanes and UFOs. Emily lives (and paints) in her hometown of Atlanta, GA, the best city in the world, with her husband Ryan who’s a pretty cool guy.
Watch live: March 2 | 2.00PM ET
Stop Making Data Scientists Do Systems
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.