Managing the hardware, drivers, libraries and packages that make up a ML development environment can be hard.

In this talk, I will introduce how Docker can be used to simplify the process of setting up a local ML development environment, and how we can use Kubernetes and Kubeflow to scale that standardised environment to provide scalable, web-based Jupyter environments for a large number of users, that can be served from both public cloud providers and from on-premise clusters.

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Further reading about Docker, Kubernetes and Machine Learning

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Python for Data Science and Machine Learning Bootcamp

☞ Machine Learning, Data Science and Deep Learning with Python

☞ Deep Learning A-Z™: Hands-On Artificial Neural Networks

☞ Artificial Intelligence A-Z™: Learn How To Build An AI

☞ A Complete Machine Learning Project Walk-Through in Python

☞ Machine Learning In Node.js With TensorFlow.js

☞ Docker for Absolute Beginners

☞ An illustrated guide to Kubernetes Networking

#machine-learning #docker #kubernetes #deep-learning #artificial-intelligence #data-science