This is a cross-platform framework for building, training, and deploying custom machine learning models within the .NET ecosystem. It provides a predictive modeling engine for classification, regression, and forecasting tasks, alongside an inference runtime to generate predictions across different hardware architectures.
The framework includes a gradient boosting library and supports interoperability with external models via a standardized open format. It features tools for prediction explainability, allowing the analysis of feature importance to debug model behavior and identify bias.
The project covers the full machine learning lifecycle, including data transformation pipelines for preprocessing tabular data, deep learning model execution for entity recognition and object detection, and native analytics kernels to accelerate training and inference.
The repository includes command-line scripts for dependency restoration and batch compilation of its source code.