Being well informed: Building a ML Model Observability Pipeline


Model Observability is often neglected but plays a critical role in ML model lifecycle. Observability not only helps understand a ML model better, it removes uncertainty and speculation giving a deeper insight into some of the overlooked aspects during model development. It helps to answer the "why" narrative behind an observed outcome. In this tutorial, we will build a production quality Model Observability pipeline with open source python stack. ML engineers, Data scientists and Researchers can use this framework to further extend and develop a comprehensive Model Observability platform.


Anindya Saha is a Staff Machine Learning Platform Engineer @Lyft, focusing on distributed computing solutions for machine learning and data engineering. He led and implemented the Spark on Kubernetes support on ml platform for feature engineering at scale with ephemeral Spark clusters on k8s. He is currently working on enabling scalable distributed model training on the ML platform.

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