January 7, 2020

171 words 1 min read

Machine learning at scale with Kubernetes

Machine learning at scale with Kubernetes

Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs.
Talk Title Machine learning at scale with Kubernetes
Speakers chris cho (Google)
Conference JupyterCon in New York 2018
Conf Tag The Official Jupyter Conference
Location New York, New York
Date August 22-24, 2018
URL Talk Page
Slides Talk Slides
Video

Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs. Along the way, Chistopher covers Kubeflow, Google’s open source solution for managing machine learning with TensorFlow in a portable, scalable manner, and shares recent innovations in monitoring GPUs with Kubernetes, smarter serving with GPUs, autoscaling from and to zero instances, and a declarative approach to portable distributed training. Join in to learn how to get started 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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