February 3, 2020

204 words 1 min read

Machine learning at scale with Kubernetes

Machine learning at scale with Kubernetes

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.
Talk Title Machine learning at scale with Kubernetes
Speakers chris cho (Google), David Sabater (Google)
Conference Artificial Intelligence Conference
Conf Tag Put AI to Work
Location London, United Kingdom
Date October 9-11, 2018
URL Talk Page
Slides Talk Slides
Video

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.

api autoscaling google gpu tosca tensorflow ml open source deep learning machine learning monitoring scalable pipeline kubernetes
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