aai-institute/beyond-jupyter
Software design principles for machine learning applications
This project helps machine learning practitioners improve the underlying software design of their models and applications. It takes unstructured or 'notebook-style' code and guides you through transforming it into well-engineered, maintainable, and efficient solutions. The target audience is data scientists, ML engineers, or researchers who develop machine learning models and want to ensure their code is robust and production-ready.
378 stars. No commits in the last 6 months.
Use this if you are developing machine learning models and want to move beyond quick, experimental notebooks to create well-structured, maintainable, and scalable applications.
Not ideal if you are looking for a new machine learning algorithm, a pre-trained model, or solely need to perform quick, one-off data analysis without long-term maintenance in mind.
Stars
378
Forks
25
Language
Jupyter Notebook
License
CC-BY-SA-4.0
Category
Last pushed
Aug 19, 2025
Commits (30d)
0
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