An architectural view of ML.NET: machine learning without leaving the .NET ecosystem

Abdul Rahman breaks down the architecture of ML.NET — Microsoft's open-source, cross-platform machine learning framework that lets you train, evaluate, and deploy ML models in familiar C# without a Python runtime or third-party libraries. He suggests splitting the solution into three projects: a trainer (a console app that loads data, trains, and saves the model), a schema library with shared data types, and a client (Web API, Blazor, or Worker) that runs predictions. At its core are MLContext as the entry point (analogous to EF's DbContext), the lazy tabular IDataView abstraction, and pipelines composed via a chainable Append and executed on Fit(), much like LINQ. Trainers are grouped by task type — classification, regression, clustering, anomaly detection, ranking, forecasting. For ASP.NET Core, PredictionEnginePool provides thread-safe predictions and hot model reload.