Kubernetes promises to be a multiworkload platform. Christopher Cho details how to leverage Kubernetes and the mighty Kubernetes APIs to build a complete deep learning pipeline, from data ingestion and aggregation to preprocessing and ML training to serving. Along the way, Christopher covers Kubeflow, a Google open source solution for managing machine learning with TensorFlow in a portable, scalable manner. You’ll also explore recent innovations around monitoring GPUs with Kubernetes, smarter serving with GPUs along with autoscaling from and to zero instances, and a declarative approach to portable distributed training. Join in and learn how to get going with just three commands across a variety of platforms with Kubernetes and Kubeflow.
Christopher Cho is a product manager and cloud program manager at Google, where he helps customers solve machine learning and infrastructure problems, and is one of the product managers in Kubeflow team. Previously, Chris was research program manager at DeepMind, working on cutting-edge ML research. His background is in enterprise business consulting. Chris is currently working toward his MSCS at Georgia Tech. He holds a BS in mechanical engineering from the University of Illinois Urbana-Champaign.
David is a Customer Engineer on Google’s Cloud Platform team, focusing on big data & machine learning. He helps customers build awesome data driven solutions through Proof of Concepts, workshops, online content, and events. Before Google, he was associate partner and co-founder of Data Reply UK. When he’s not helping customers he can be found with his family cycling around Greenwich park, traveling or eating international food
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