This repository is maintained by the MLPro team and provides two core building blocks:
- 📚 Howtos – executable guides for working with MLPro modules, tools, and configurations
- 📊 Benchmarking Suite – a structured system for evaluating MLPro setups, including:
- Benchmark Scenarios – predefined configurations for standardized testing
- Benchmark Tests – executable cases to compare algorithms and measure performance
All content is developed and maintained to showcase selected features, evaluate algorithms, and document recommended workflows.
...
...
MLPro - The integrative middleware framework for standardized machine learning in Python
South Westphalia University of Applied Sciences, Dept. of Automation Technology and Learning Systems
We welcome community contributions that align with our structure and quality standards.
Before submitting a pull request, please read our contribution guidelines.