SE-ML/awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications.
This is a curated list of articles and resources designed for professionals building and maintaining machine learning applications. It compiles best practices for all non-core ML aspects, from data handling and model training to deployment and team collaboration. The output is a collection of knowledge that helps ML engineers, data scientists, and MLOps specialists improve the robustness, reliability, and maintainability of their production ML systems.
1,345 stars. No commits in the last 6 months.
Use this if you are developing or managing machine learning projects and want to ensure they are built with strong software engineering principles.
Not ideal if you are looking for new machine learning algorithms or research on core ML model development.
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Mar 26, 2024
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