Using Docker and Kubernetes to Simplify Machine Learning

Using Docker and Kubernetes to Simplify Machine Learning

Using Docker and Kubernetes to Simplify Machine Learning: 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.

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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