machinelearning

Why we included this project

ML.NET is the pick for teams that build .NET applications and want machine learning without adopting a second language or standing up a separate model-serving service. Data loading, transformations, training, and evaluation all live behind one .NET-native API, so you can build models for classification, regression, clustering, or anomaly detection and call them from the same codebase you already ship. If your data scientists prototype in Python, the framework still helps: it loads TensorFlow and ONNX models, so the inference step can run inside your .NET services. A trained model serializes to a binary file that other applications can reload, which makes the artifact easy to move between environments. For teams that want to stay within the Microsoft stack, this is the most direct route from tabular data to a working predictor.