ML.NET

Trigger On

• integrating machine learning into a .NET application
• training or retraining ML.NET models from local data
• reviewing inference pipelines, model loading, or AutoML-generated code

Workflow

1. Start from the prediction task and data quality, not the algorithm or package list.
2. Separate training code from inference code so the production path stays lean and predictable.
3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.
5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.

Deliver