AI-Based Framework for Predicting Machine Learning Model Failure Using Data Quality, Distribution Shift and Prediction Uncertainty

An AI-based framework that predicts machine learning model failure from data quality, distribution shift and prediction uncertainty.

An AI-based framework that predicts machine learning model failure from data quality, distribution shift and prediction uncertainty. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.