AMLE is a simple unopinionated framework for experimenting with machine learning (ML). I built it to help me learn ML, and to reduce my workload running ML experiments, by automating repeatable tasks.
It is a perfect example of Not Invented, Here as you can find more performant and fully featured environments elsewhere, so please consider alternatives like R, or existing Python ML libraries.
The code is still very much under construction.
It is built to the following (aspirational) principles:
- Generic. Just a shim, does not contain ML code, and tries to not be opinionated about how ML works or data types
- Reproducibility. Run the same test with same inputs and get the same output(s) - or at least statistically similar.
- Reduce experimentation work effort. Support comparative testing across different parameters and/or ML algorithms, retains historical parameters and results
- Add value to experimentation. Support evolutionary genetic approach to configuring algorithm parameters (TBD)
- Visibility. Make it easy to understand how experiments are running / ran (TBD)
Documentation may one day be available on Read the Docs