High-Throughput Graph Abstraction at Netflix: Part I
By Oleksii Tkachuk, Kartik Sathyanarayanan, Rajiv ShringiIntroductionNetflix has a diverse range of graph use cases, each serving specific business needs with unique functionality and performance requirements. These use cases fall into two broad categories:OLAP: These use cases typically involve ope...
Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use
Authors (listed alphabetically)Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le ZhangCore ML Infra team: Eric Shang, Pihui WeiML Data team: Connor Votroubek, Yi HeUser Understanding team: Camilo Munoz, Simin LiIf you work on ranking, retrieval, or recommendation systems, you’ve probably ...
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...