Jupyter notebooks simplify the process of developing and sharing Data Science projects across groups and organizations. However, when we want to deploy our work into production, we need to extract the model from the notebook and package it up with the required artifacts (data, dependencies, configurations, etc) to ensure it works in other environments. Containerization technologies such as Docker can be used to streamline this workflow.
This hands-on tutorial presents Docker in the context of Reproducible Data Science - from idea to application deployment. You will get a thorough introduction to the world of containers; learn how to incorporate Docker into various Data Science projects; and walk through the process of building a Machine Learning model in Jupyter and deploying it as a containerized Flask REST API.
I am looking for editors/curators to help with branches of the tree. Please send me an email if you are interested.