Talk on Operationalizing Machine Learning: An Interview Study + Panel Discussion
We are discussing the results from a semi-structured interview study of ML engineers spanning different organizations and applications to understand their workflow, best practices, and challenges. The authors found that "successful MLOps practices center around having high velocity, validating as early as possible, and maintaining multiple versions of models for minimal production downtime." In addition, we invited a panel of industry experts to discuss MLOps pain points and check in on their MLOps tooling advice.
Introducing our speaker: Rolando Garcia!
Rolando Garcia is a Ph.D. candidate at UC Berkeley working in the EPIC Data Lab, where he researches experimentation in MLOps.
Join us to hear all about Rolando and et al.’s “Operationalizing Machine Learning: An Interview Study” paper, followed by a panel discussion.