Effective Observability for MLOps pipelines at scale video

In this video, Rishit delves into effective observability for Machine Learning (ML) pipelines at scale. He explains the crucial differences between observability and monitoring in ML applications, highlighting challenges such as distribution shifts, feedback loops, and the inadequacy of traditional metrics. Rishit also demonstrates practical techniques for logging and interpreting system metrics, model-related metrics, and feature distributions. This comprehensive guide provides valuable insights for maintaining the reliability and performance of ML models.