Docker for data science

Why is docker important in data science?

In my previous post, I wrote on Understanding MLOPS but in order to become a maestro at machine learning operations, you need to understand docker and why it is important in data science.

Ever developed a machine learning model and then changed laptop only to realise your code is breaking and you keep running into an infinite “Import Error” statement or “Module not found”. Sometimes, It could be a version error as you are trying to run your code on a different python package version. The solution for this is Docker.

Before we get into it. Check out more data science related articles I have written here and also to get more interesting tutorials follow me on medium.

So What is Docker?

Docker is a tool for creating and deploying isolated environments for running applications with their dependencies. Basically, Docker makes it easy to write and run codes smoothly on other machines with different operating systems by putting together the code and all its dependencies in a container.