santiagxf/mlproject-sample
Sample repository about how to structure an ML project using software engineering practices
This project offers a structured approach to building machine learning models for predicting outcomes like car prices. It helps data scientists and ML engineers organize their code, data, and experiments using best practices, combining interactive notebook development with robust software engineering principles. You start with raw data, build a predictive model, and end up with a well-organized, reproducible project that can be easily trained and deployed.
No commits in the last 6 months.
Use this if you are an ML engineer or data scientist looking for a clear, opinionated framework to structure your machine learning projects, especially when moving from experimental notebooks to more production-ready code.
Not ideal if you are a business user simply looking for a ready-to-use model or a pure researcher who doesn't need to consider deployment or software engineering practices.
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Jupyter Notebook
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Last pushed
Aug 11, 2022
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