Making AI More Accessible to the Non-Developer
summary
Claim: non-developers use ML workflows without writing code by providing clearer APIs and automation. Concrete work: wrapped scikit-learn and TensorFlow in Python with early, human-readable error messages; added parallel learner comparison with confusion matrices and simple visualizations so multiple models run and get compared; built automated feature transforms and explainable transform logs that generate candidate features and report exactly what was tried. The system reduces the time engineers spend sifting "bajillions" of rows and surfaces actionable rules.
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