This video will explain why and how Kubernetes is well suited for training and running your machine learning models in production. Specifically it will show how to setup a variety of open source machine learning frameworks such as TensorFlow, Apache MXNet and Pytorch on a Kubernetes cluster.
Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally and data intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes.
Attendees will learn 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.
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Further reading about Kubernetes and Machine Learning
☞ An illustrated guide to Kubernetes Networking
☞ Kubernetes Tutorial - Step by Step Introduction to Basic Concepts
☞ Top 18 Machine Learning Platforms For Developers
☞ Top 10 Machine Learning Algorithms You Should Know to Become a Data Scientist
☞ Machine Learning A-Z™: Hands-On Python & R In Data Science