The Importance of Data Science Versional Control System to Machine Learning and MLOps

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The Importance of Data Science Versional Control System to Machine Learning and MLOps

xpresso.ai Team
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Machine Learning is not a pipelined process. You don’t just do it once and then stop. You need to iterate consistently to produce the best results in this field. That is why data science version control systems are so crucial. It is similar to the software engineering process with tools like git. However, machine learning is different in the fact that you don’t just have code to analyze. Software engineering only requires code, and version control systems are built to accommodate that. For machine learning, you need a system that will manage your code, model, and data. All of these things have to work in lockstep for the software to work. It requires much more iteration than software engineering, and you also need to tie the code, data, and model together before you can know how accurate your finished program is.

Comparing Data Science Version Control to Traditional a Version Control System

As mentioned above, the main difference between a traditional version control system and a data science version control system is that the data science version control requires managing many more data types. You need to manage more than code, and the file formats are not always the same. It means your version control system has to be flexible enough to adapt to everything that the data scientists and machine learning engineers have added. The two also have to be flexible enough to allow many processes to be automated. The automation will ensure that engineers can easily access their code, data, and models to quickly see what changes need to be made to improve results.

Managing Code

During the machine learning process, both the data scientists and machine learning engineers will create code. The modeling code and the deployment code are usually in different languages, and this is something a data science version control system needs to consider. It also needs to manage the metadata between these two types of code, so you can easily see changes that were made. 

Managing Data

Data is usually one of the biggest problems with these version control systems. This is because the data can be in many different formats. For example, your data could be text files, or it could even be image files. Your system needs to be able to manage all of these things. Your system also needs to be able to manage data, no matter the size. You will have files that are just a few bytes large, or it could even have files that are many gigabytes large. This system needs to perform optimally in all situations.

Managing Your Models

Machine learning engineers often update models as time goes on. The data scientists are constantly testing and tweaking things to create the best model from the data available. A data science version control system has to make it easy to manage changes to models over time. It has to make looking at models as easy as it is to change software code when software engineering.

Conclusion

Data science version control is a crucial technology to the machine learning process. It allows organizations to version their data, code, and models in the same way that software engineers can do for their code.

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xpresso.ai Team
Enterprise AI/ML Application Lifecycle Management Platform