Packaging your code

In some ways, it is interesting that Python has taken the world by storm. It is dynamically typed and non-compiled, so it can be quite different to work with compared to Java or C++. This particularly comes to the fore when we think about packaging our Python solutions. For a compiled language, the main target is to produce a compiled artifact that can run on the chosen environment, a Java jar for example. Python requires that the environment you run in has an appropriate Python interpreter and the ability to install the libraries and packages you need. There is also no single compiled artifact created, so you often need to deploy your whole code base as is.

Despite this, Python has indeed taken the world by storm, especially for ML. As we are ML engineers thinking about taking models to production, we would be remiss to not understand how to package and share Python code in a way that helps others to avoid repetition, to trust in the solution, and to be able to easily...