Whether starting from a template or starter, you can save time by keeping some handy bash functions around to track down dependencies, find code references and more.

Why use containers at all? Containers can simplify your machine learning workflow and help you manage environments in a way that is easily configurable, shareable and fast.

How to extend the official Python Docker image to create an ML image with specified dependencies. These practices could be extended to other kernels (e.g., R or Julia).