ML.NET

ML.NET is an open-source, cross-platform machine learning framework purpose-built for the .NET ecosystem, with full support for C# and F#. It enables developers to build, train, and deploy custom machine learning models directly within their existing .NET applications, without requiring external dependencies or language switching.

The framework supports core machine learning tasks including regression, binary classification, multiclass classification, clustering, anomaly detection, and recommendation systems. These algorithms operate on structured data in formats like CSV, SQL databases, or in-memory collections.

ML.NET also supports advanced scenarios through deep learning integration. You can perform image classification and object detection using TensorFlow and ONNX models, process natural language with text classification and sentiment analysis, and implement time-series forecasting for sequential data prediction. The framework also enables transfer learning and fine-tuning of pretrained models, allowing you to adapt existing neural networks to your specific domain with reduced training data and computational requirements.

The AutoML API automates the complete model building lifecycle, including feature engineering, algorithm selection, hyperparameter tuning, and model evaluation. This automation identifies optimal model configurations for your specific dataset and prediction task while maintaining full programmatic control when needed. Developers can also manually configure pipelines using the comprehensive set of transforms and trainers exposed through the API.