Have you been looking out for project folder structure or template for storing artifacts of your data science or machine learning project? Once there are teams working on a particular data science project and there arises a need for governance and automation of different aspects of the project using build automation tool such as Jenkins, one would feel the need to store the artifacts in well-structured project folders. In this post, you will learn about the folder structure using which you could choose to store your files/artifacts of your data science projects.
The following represents the folder structure for your data sciences project.
Note that the project structure is created keeping in mind integration with build and automation jobs.
If you are building machine learning models across different product lines, here could be the folder structure:
The following are the details of the above-mentioned folder structure:
In this post, you learned about the folder structure of a data science/machine learning project. Primarily, you will need to have folders for storing code for data/feature processing, tests, models, pipeline and documents.
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