Rajeev Prabhakar
Rajeev is a Senior software engineer at Lyft focused on building ML observability platform. Prior to Lyft, Rajeev has spent the last few years working on building ML platforms, enabling large scale distributed computing on k8s and building realtime ultra low latency systems.
Sessions
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