Tips for managing several ML project with similar framework

Hi,

I'm working on an end-to-end ml project which, for the moment, goes from training (it takes already processed train/val/test data from an S3 bucket) to deploy, passing through hyperparameter tunning. This project has been developed on SageMaker Studio and in the beginning, I decided to keep track on the project with a github's repository.

So, my work has certain degree of maturity: I was able to successfully train, tune, deploy and infer over a dataset. But the troubles came when I try to replicate this work for another dataset (a new project with a similar framework).

Let's say that I had a client "A" for which I developed this project and now I have a new client "B" with similar requirements than client A. I'm looking for the best way of "copy & paste" the project considering the following:

a) I would like to keep working on the repository. The idea here is the repo been like a project's template ir order to clone the repository, make some few corrections (changing model's name, working bucket, etc) and then execute tuning, evaluation and deploy. b) There's a lot of changes and improvements that I should make in the future. So, I'd like those changes been reflected on both projects.

If anyone could give me some tips, guidelines, share his experience with something like this I would be very grateful.

Regards! :D