By John Burns and Emily YuanIntroductionAt Netflix, we operate using a polyrepo strategy with tens of thousands of Java repositories. This means that we need to have ways of sharing common build logic across these repositories. On the JVM Ecosystem team within Java Platform, we build tooling such as...
Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph
Saish Sali, Nipun Kumar, Sura ElamuruguIntroductionAs 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 focu...
By Nipun Kumar, Rajat Shah, Peter ChngIntroductionThis is the first blog post in a multi-part series that shares technical insights into how our ML model serving infrastructure powers several personalized experiences at scale across various domains (e.g., title recommendations, commerce). In this in...
Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer
Guangtong Bai | Staff Software Engineer, Product ML Infrastructure*; Shantam Shorewala | Software Engineer II, Product ML Infrastructure*; Chi Zhang | Staff Software Engineer, AI Platform*; Neha Upadhyay | Software Engineer II, AI Platform*; Haoyang Li | Director, Product ML Infrastructure*These aut...
Orchestrating Media Workflows Through Strategic CollaborationAuthors: Eric Reinecke, Bhanu SrikanthIntroduction to Content Hub’s Media Production SuiteAt Netflix, we want to provide filmmakers with the tools they need to produce content at a global scale, with quick turnaround and choice from an ext...
The Human Infrastructure: How Netflix Built the Operations Layer Behind Live at Scale
By: Brett Axler, Casper Choffat, and Alo LowryIn the three years since our first Live show, Chris Rock: Selective Outrage, we have witnessed an incredible expansion of our live content slate and the live operations that support it. From modest beginnings of streaming just one show per month, we are ...