d2m streamlines the process of transforming raw data into fully trained machine learning models.
Tailor the pipeline to your specific needs, and easily develop models for any type of tabular and time series data.
Through integration with CodeCarbon, d2m actively tracks and reports on carbon emissions, promoting environmentally responsible AI development.
Incorporating tools like SHAP and LIME, d2m ensures that AI decisions are transparent and understandable.
Utilizing Bayesian dropout techniques, it provides reliable uncertainty estimates for neural network predictions.