Making Failure Diagnosis Reliable with Pensive AI

By Praneeth Yenugutala , Victor Dusa , Anastasia Samoilova , Megan Ren , Justin Wei , Marcelo Mayworm At Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such as Titus, Spark, Trino, and Druid. On peak days, roughly 100,000 workloads fail. When workloads fail, the failure itself is only the beginning.