Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)

Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)

Wednesday, March 20
2:00 PM - 2:40 PM
Room 124-125

Data Science, Machine Learning, and Artificial Intelligence has exploded in popularity in the last five years, but the nagging question remains, “How to put models into production?” Engineers are typically tasked to build one-off systems to serve predictions which must be maintained amid a quickly evolving back-end serving space which has evolved from single-machine, to custom clusters, to “serverless”, to Docker, to Kubernetes. In this talk, we present KubeFlow- an open source project which makes it easy for users to move models from laptop to ML Rig to training cluster to deployment. In this talk we will discuss, “What is KubeFlow?”, “why scalability is so critical for training and model deployment?”, and other topics.

Users can deploy models written in Python’s skearn, R, Tensorflow, Spark, and many more. The magic of Kubernetes allows data scientists to write models on their laptop, deploy to an ML-Rig, and then devOps can move that model into production with all of the bells and whistles such as monitoring, A/B tests, multi-arm bandits, and security.

Presentation Video


Trevor Grant
Data Scientist and Engineer
Trevor Grant is PMC Member of the Apache Mahout and Apache Streams projects. He is a tinker extraordinaire and does a poor job of documenting his projects on He has an M.S. of Applied Math, a dog, a cat, an M.B.A., and a home in Chicago. He speaks a fair amount at locations internationally, and in general his talks are usually pretty fun.
Holden Karau
Developer Advocate
Holden is a transgender Canadian open source developer advocate @ Google with a focus on Apache Spark, BEAM, and related "big data" tools. She is the co-author of Learning Spark, High Performance Spark, and another Spark book that's a bit more out of date. She is a commiter on and PMC on Apache Spark and committer on SystemML & Mahout projects. She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal.