No Python, No PhD: Train Real ML Models in C# with ML.NET (Regression, Classification, Clustering)

Mattrx ran its predictive features on a Python scikit-learn microservice for two years. We replaced it with ML.NET running in-process inside the existing .NET 9 app — and trained real regression, classification, and clustering models in C# with no separate service, no second language in production, and no implementing gradient descent by hand. If you're a .NET team that "does ML" by shipping a Python sidecar, you're paying a tax most teams never question: a second runtime, a second deploy pipeline, a second on-call surface, a cross-process hop on every prediction, and a data contract that drifts between two languages. For classical ML — regression, classification, clustering — you usually don't need any of it. Before vs after Dimension Before (Python sidecar) After (ML.NET in-process) Languages in production C# and Python C# only Deploy pipelines 2 1 Prediction path HTTP to Flask -> scikit-learn in-memory call Prediction p95 45 ms 2.8 ms Model artif