Figuring out applied ML: Building frameworks and teams to operationalize ML at scale.

Solving the hard problems requires operationalizing ML at scale. Doing that in a definable and repeatable way takes planning and practice. Understanding how to match the deep understanding of subject matter experts to the technical application of ML programs remains a real barrier to applied ML in the workplace.
Who is this presentation for?
People thinking about how to measure and evaluate ML engagements or build ML teams
Prerequisite knowledge:
None
What you'll learn?
Adventure: ML team building and ML KPIs

Dr. Nels is a clinical systems IT director at a fortune 10 company, the author of Graduation with Civic Honors, an avid writer, tech chaser, sports card collector, and a major TensorFlow enthusiast.