Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph

The useful part

Saish Sali, Nipun Kumar, Sura Elamurugu Introduction As Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of our business. When Netflix began investing in machine learning over a decade ago, it was primarily focused on a single domain: personalization. Scala was the industry standard, our ML teams were relatively small, and optimizing member engagement was our primary use case.

How it works

• While this diversity is a testament to how machine learning has evolved to drive value across many verticals at Netflix, this growth introduces a new challenge: enabling cross-pollination of models and data...
• Without any discovery infrastructure, ML practitioners couldn't easily collaborate or share work across business verticals.
• Fast forward to today, and machine learning has become the backbone of Netflix's business transformation.
• Optimizing engagement and helping members discover content they'll love Studio:
• The Challenge: A Fragmented ML Landscape As our ML investments scaled across these domains, a critical problem emerged: the models produced largely became black boxes.

Details worth keeping

Our newest domain, requiring real-time decisioning and targeting … and a growing number of additional use cases across the company Each domain operates with a different tech stack, different business metrics, and a distinct organizational structure.