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Kubernetes provides isolation, auto-scaling, load balancing, flexibility, and GPU support. These features are critical to run computationally, data-intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors makes it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes.

This talk will explain why and how Kubernetes is well suited for single and multi-node distributed training, deploying your machine learning models in production and setting up visualization tools like TensorBoard for monitoring. Specifically it will show how to set up a variety of open-source machine learning frameworks such as TensorFlow on a Kubernetes cluster. The attendees will learn distributed training, massaging and inference phases of setting up a Machine Learning framework on Kubernetes. Attendees will leave with a GitHub repo of fully working samples.

Speaker:
Arun Gupta
Principal Technologist
Amazon Web Services
Profile: https://aws.amazon.com/developer/community/evangelists/arun-gupta/
Speaker Bio: Arun Gupta is a Principal Open Source Technologist at Amazon Web Services. He has been building and leading developer communities for 10+ years at Sun, Oracle, Red Hat and Couchbase. He has deep expertise in leading cross-functional teams to develop and execute strategy, planning, and execution of content, marketing campaigns, and programs. Prior to that he led engineering teams at Sun and is a founding member of the Java EE team.