Developing and maintaining even a single production-ready model can be a challenge for data science teams, let alone hundreds of models. Our session on effective strategies to scale ML will focus on quality control for organizations that deal with deploying a large number of models at scale.

Panelists:
Rosa Català, Director of ML Content & Platforms, Reddit
Ram Bala, Sr. Principal Data Scientist, Equinix
Jing Huang, Sr. Director of ML Engineering, Momentive.ai
Xuerui Wang, Sr. Director of Engineering, Uber
Dan Friedman, VP of Data Science, Expedia Group
Rohin Bansal, Director, Data Governance, TELUS

Moderator:
Kojin Oshiba, Co-Founder, Robust Intelligence

00:00 Speaker introductions
08:02 What are the challenges and risks as you scale ML models and teams?
24:07 How can you help new data scientists establish a business value mindset?
35:27 How can a hub and spokes model benefit a data science organization?
42:31 What advice can you share for instilling integrity when working with ML models at scale?