A Primer on Version Control for Data Science and Machine Learning Projects

Unlike software engineering, machine learning requires you to keep track of more than just code. Version control systems like Git only need to keep track of text data in the form of code, configuration files, and small data files. It often needs to keep different versions of these files, making the job a lot easier for the person creating the version control system. Machine learning and data science require more than just this. You need to keep track of your code, your model, and your data.

All of these different aspects are crucial to developing machine learning programs. The data science version control system needs to be robust enough to deal with all of these issues. Every data science version control implementation must seamlessly manage all three aspects at the same time. In reality, the version control system in MLOps needs the foundation of the version control system in software engineering and new features to make it robust enough for machine learning.

There’s also the fact that developing machine learning models and implementing them is an iterative process. You have to go to the entire pipeline while testing everything again and again. You continually refine and fine-tune your code, data, and models to ensure that they are as accurate as possible. This iterative process also places further strain on your data science version control system. It needs to be able to version everything in such a way that it will be useful in this iterative process.

Versioning Your Model

The result of every machine learning project is a model that can be integrated into a software application. The model contains the knowledge that you have spent time and effort developing. All the data you have collected, and machine learning algorithms, then go into building the model. You might need to couple the model with your code, or you might need to have metadata along with the model. Either way, data science version control systems need to account for all of this and manage all of it effectively. Versioning also has to account for data drifting.

Code Version Control

Code in machine learning is very similar to software engineering. However, there are key differences that involve the variety of languages that need to be used. You need to account for these differences when understanding version control and machine learning. There are many different types of code that you need to implement in your application. For example, there will be glue code that ties your machine learning model to the application, and there were also be the code used to develop the model. Sometimes, these two different codes are in different languages. You also have to factor in the difference between interpreted languages and compiled ones. Compiled languages like C++ will need the libraries you use along with the code. The version control needs to manage these dependencies.

Data in Your Data Science Version Control System

The heart of every machine learning application is good data. Data comes in many forms, and the version control system has to manage each type. For example, the system might need to manage video and audio data when building models from these. It also has to keep detailed information about metadata and records. In most machine learning applications, the data science version control system also has to manage large files.

Data science version control systems are a crucial part of the iterative process that is machine learning model development. It is also a crucial component of the developing field of MLOps.