Updating ML.NET Models: Adding Fields and Adjusting Pipelines

Machine learning models are rarely static; as your application data evolves, your model's schema must adapt. This text-based course guides you through the essential process of updating existing ML.NET models to accommodate new data structures and business requirements.\n\nYou will transition from working with rigid, outdated models to confidently modifying features, changing label fields, and restructuring your C# training pipelines for better predictions.\n\nWhat you'll learn:\n- Understand the core architecture of ML.NET pipelines, data views, and schema definitions.\n- Add new feature and label fields to existing model input and output classes in C#.\n- Modify and map data types to ensure compatibility with ML.NET estimators.\n- Adjust the training pipeline configuration to incorporate newly added data fields.\n- Apply modern ML.NET evaluation metrics to verify model performance after schema changes.\n- Practice updating model schemas through step-by-step written code walkthroughs.\n\nStarting with foundational ML.NET concepts and C# data structures, this course walks you through the practical mechanics of schema evolution, pipeline reconfiguration, and model retraining.\n\nThis course is designed for C# developers and software engineers who are new to machine learning or looking to maintain and update existing ML.NET implementations. No prior machine learning experience is required, though basic familiarity with C# is helpful.\n\nStart learning today and keep your C# machine learning models aligned with your evolving application data.