From Silo to Collaboration - Building Tooling to Support Distributed ML Teams at Twitch
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
Explore the journey of Twitch's machine learning teams as they transition from siloed operations to collaborative efforts in this insightful conference talk. Discover how Twitch addresses the challenges of distributed ML teams by developing innovative tooling and infrastructure solutions. Learn about the company's ML team structure and the strategies implemented to support efficient ML development across product areas. Gain valuable insights into Twitch's feature store, which serves as a centralized control plane while enabling distributed feature ownership. Examine the in-house ML orchestration system, Conductor, and its role in promoting best practices for pipeline management. Understand how Twitch fosters a collaborative ML culture among engineering teams, drawing parallels to community-owned open source projects. Delve into the benefits of cross-team contributions and development in enhancing ML capabilities across the platform.
Syllabus
From Silo to Collaboration - Building Tooling to Support Distributed ML Teams at Twitch
Taught by
Toronto Machine Learning Series (TMLS)
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent