An Intuitive Guide to Docker for Data Science

Learn the basic concepts of Docker, common commands, and how to dockerize a machine learning application

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While working as data scientist, it’s important to write code that runs in any operating system with all the necessary dependencies, and is ready to be deployed on the cloud. Despite your efforts, it may still not work and you may lose time to understand what the problem is.

What tool can we use to avoid this struggling? Docker is the solution to your problems. Using docker, you can easily obtain a robust environment for your data science project, without becoming crazy.

In this article, I am going to explain about Docker’s main concepts, the most common commands and a quick example of dockerized machine learning application. Let’s get started!

Table of contents:

• What is Docker?
• Basic concepts of Docker
• Virtual Machine Vs Container
• Setting Up Docker
• Dockerize a ML application
• Summary of Docker commands
• Limitations of Docker

What is Docker?