Explore the intricacies of building and managing machine learning teams in this comprehensive lecture. Gain insights into the challenges of ML product management, understand various roles within ML teams, and learn strategies for hiring and getting hired in the field. Discover organizational archetypes ranging from ad hoc ML to ML-first approaches, and delve into effective team building and management techniques. Uncover best practices for ML project management, including "managing up" and educating organizations about ML. Investigate the concept of "Agile for ML" and explore essential principles for ML product design. Access detailed notes and slides for further study, and subscribe to follow along with the full course.
Overview
Syllabus
Why managing ML products is hard
Roles in ML teams: MLEs, MLRs, DSs
Hiring and getting hired in ML
Organizational archetypes: from ad hoc ML to ML-first
Building ML teams
Managing ML teams and products
How to manage ML projects better
"Managing up" in ML
ML PMs are well-positioned to educate the org
What is the "Agile for ML"?
Best practices for ML product design
Summary
Taught by
The Full Stack
