Data collection, preprocessing, feature engineering are the fundamental steps in any Machine Learning Pipeline. After feature engineering, being able to parallelize training on multiple low cost machines helps to reduce cost and time both. And, then being able to train models in a distributed manner speeds up Hyperparameter Tuning. How can we unify these stages of ML Pipeline in one unified distributed training platform together? And that too on Kubernetes?

Our ML platform is completely based on Kubernetes because of its scalability and rapid bootstrapping time of resources. In this talk we will demonstrate how Lyft uses Spark on Kubernetes, Fugue (our home grown unifying compute abstraction layer) to design a holistic end to end ML Pipeline system for distributed feature engineering, training & prediction experience for our customers on our ML Platform on top of Spark on K8s. We will also do a deep dive to show how we are abstracting and hiding infrastructure complexities so that our Data Scientists and Research Scientist can focus only on the business logic for their models through simple pythonic APIs and SQL. We let the users focus on ''what to do'' and the platform takes care of ''how to do''. We will share our challenges, learning and the fun we had while implementing. Using Spark on K8s have helped us achieve large scale data processing with 90% less cost and at times bringing down processing time from 2 hours to less than 20 mins.

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