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.


Rajeev Prabhakar is a Machine Learning Platform Engineer at Lyft. Currently he is focused on building model observability at scale for a wide range of ML applications across teams at Lyft. Prior to Lyft, he worked at Quantcast on the ML platform team. Enabling distributed computing with spark and notebooks on k8s, building control systems for optimal spend budget allocation and optimising real time prediction latency in a low latency serving environment are some of the things he worked on.

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