ABOUT THE TALK:
Our traditional testing and CI methods for Data Science are not working, but we can't just give up on providing guardrails.

As engineers, how do you solve ML testing?

In this talk, Emily Curtain discusses: - abstracting, decoupling, and separating concerns - keeping pytest only where it belongs - substituting testing for observability in appropriate places - applying data reliability practices and thereby solving some problems at the source - by honoring Data Scientists' mental models, and ways of working

ABOUT THE SPEAKER: Emily Curtin is a Staff MLOps Engineer at Intuit Mailchimp. She leads a crazy good team focused on helping Data Scientists do higher quality work faster and more intuitively.

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