IBM/data-science-best-practices
The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.
This project provides guidelines and examples for data scientists and machine learning engineers to build robust data science solutions. It helps transition experimental models into reliable, production-ready applications. The main output is a structured approach to developing scalable, versioned, and monitored data science use cases.
No commits in the last 6 months.
Use this if you are a data scientist or ML engineer looking to move your data science projects from development into a production environment.
Not ideal if you are looking for a software library or code to directly implement a specific algorithm or model.
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Language
Ruby
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CC-BY-4.0
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Last pushed
Sep 17, 2025
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