From the course: DevOps for Data Scientists

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Version control for data science models

Version control for data science models

From the course: DevOps for Data Scientists

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Version control for data science models

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- [Instructor] Building data science models is an iterative process. We build the model, evaluate it, and them make some changes and try again. This is similar to the edit-compile-execute loop common to program development. That's a good thing because that means we can use software engineering tools to help with our data science model development. One common practice in software engineering is version control. This is a process of tracking each version of a program or model in ways that make it easy to return to earlier versions, track the changes that we make in each revision, and to share code with the collaborators. One of the most popular version control services is GitHub. It's a code repository that is free to use and has been widely adopted by the software engineering community. GitHub uses the Git version control system. It has a rich set of functionality, more than we can cover here. If you'd like to learn more about Git and GitHub, I suggest courses such as Learning Git and…

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