Data Scientists at the moment are expected to put in writing production code to deploy their machine learning algorithms. Due to this fact, we’d like to pay attention to software engineering standards and methods to make sure our models are deployed robustly and effectively. One such tool that could be very well-known within the developer community is make. This a robust Linux command that has been known to developers for a very long time and in this text I would like to indicate how it will possibly be used to construct efficient machine learning pipelines.
make is a terminal command/executable similar to ls or cd that’s in most UNIX-like operating systems akin to MacOS and Linux.
Using make is to simplify and breakdown your workflow right into a logical grouping of shell commands.
It’s used widely by developers and can also be being adopted by Data Scientists because it simplifies the machine learning pipeline and enables more robust production deployment.
make is a robust tool that Data Scientists must be utilising for the next reasons:
- Automate the setup of machine learning environments
- Clearer end-to-end pipeline documentation
- Easier to check models with different parameters
- Obvious structure and execution of your project
A Makefile is largely what the make commands read and execute from. It has three components:
- Targets: These are the files you are attempting to construct or you’ve got a
PHONYgoal for those who are only carrying out commands. - Dependencies: Source files that should be run before this goal is executed.
- Command: Because it says on the tin, these are the list of steps to supply the goal.
Let’s run through a quite simple example to make this theory concrete.
smooth jazz