Containerizing

If you develop software that you want to deploy somewhere, which is the core aim of an ML engineer, then you have to be very aware of the environmental requirements of your code, and how different environments might affect the ability of your solution to run. This is particularly important for Python, which does not have a core capability for exporting programs as standalone executables (although there are options for doing this). This means that Python code needs a Python interpreter to run and needs to exist in a general Python environment where the relevant libraries and supporting packages have been installed.

A great way to avoid headaches from this point of view is to ask the question: Why can't I just put everything I need into something that is relatively isolated from the host environment, which I can ship and then run as a standalone application or program? The answer to this question is that you can and that you do this through containerization. This is...