dotnet/machinelearning

Features

• .NET Machine Learning Integrations - Integrates predictive models and machine learning capabilities directly into applications built with the .NET framework.
• Cross-Platform Inference Frameworks - Provides a runtime engine that executes trained models across diverse architectures, including ARM64 and WebAssembly.
• Custom Predictive Model Development - Enables the development of custom classification and forecasting models using built-in algorithms and transformation pipelines.
• Training - Implements classification and regression tasks using an optimized gradient boosting framework.
• Machine Learning Frameworks - Provides a comprehensive cross-platform framework for building, training, and deploying custom machine learning models in .NET.
• Model Deployment - Supports loading and executing pre-trained machine learning models from external formats within software applications.
• Model Inference Runtimes - Provides an execution engine for running trained models and external pre-trained formats across different architectures.
• Model Predictions - Generates predictions and inferences from trained models when applied to new input data.
• Prediction Engines - Ships a toolset for training classification, regression, and forecasting models using tabular data and custom algorithms.
• Model Training - Enables developing and training classification or regression models using local datasets and built-in algorithms.
• Data Preprocessing Pipelines - Provides tools for cleaning and transforming raw datasets from files or databases to prepare them for ML pipelines.
• Data Transformation - Supports importing datasets from files and databases and applying transformations to prepare raw information for training.
• Data Transformation Pipelines - Provides sequential data transformation pipelines to prepare raw datasets for model training and inference.
• Tabular Data Processors - Provides a tabular interface to filter, merge, and transform datasets for machine learning pipelines.
• Deep Learning Inference Engines - Includes runtime engines for executing deep learning models such as transformers for object detection and entity recognition.
• Gradient Boosting Libraries - Includes a library for implementing classical machine learning tasks using optimized gradient boosting algorithms.
• Feature Importance Attribution - Includes tools for analyzing the contribution of individual input variables to model predictions for debugging and transparency.
• External Model Loading - Supports importing and executing pre-trained models from standardized open formats to extend algorithmic capabilities.
• Model Interoperability Formats - Supports loading and executing machine learning models from the standardized ONNX open format for cross-framework compatibility.
• Model Explainability - Analyzes feature importance and contributions to interpret model decisions and identify potential bias.
• Dynamic Schema Inference - Automates preprocessing and training by dynamically inferring input and output shapes from the provided data.
• Native Analytics Kernels - Uses optimized low-level mathematical kernels to accelerate the execution of training algorithms and inference.
• Schema Inference - Automatically determines input and output data shapes at runtime to support datasets without predefined structures.
• Cross-Platform Runtimes - Provides a unified execution layer to run trained models across diverse hardware architectures including ARM64 and WebAssembly.
• Custom Kernel Accelerators - Implements optimized low-level mathematical kernels to accelerate the execution of training algorithms and data processing.
• General Machine Learning - Cross-platform machine learning framework for .NET developers.
• Machine Learning and Data Science - Cross-platform framework for building machine learning models.
• Model Conversion Tools - Framework for integrating machine learning into .NET applications.
• Developer Utilities - Library for integrating predictive models into applications.

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