Retraining Machine Learning Models with ML.NET

Machine learning models are not static; they degrade in accuracy as real-world data changes over time. Keeping your applications intelligent requires a robust strategy for retraining and redeploying models without rewriting your entire codebase. This text-based course guides you through the process of managing the lifecycle of your ML.NET models. You will transition from understanding basic model persistence to implementing automated retraining workflows that adapt to fresh data seamlessly. Learn the core concepts of model drift, evaluation metrics, and the machine learning lifecycle in .NET. Load and prepare new training data dynamically using C# data pipelines. Retrain existing pipelines with updated datasets to maintain high prediction accuracy. Save and serialize trained models to files or streams for efficient storage. Deploy and reload saved models into production applications for real-time inference. Monitor model performance and identify when retraining is necessary using basic MLOps principles. You will start with foundational definitions of model lifecycles before diving into step-by-step written explanations and C# code snippets. The material progresses from manual serialization to designing structured retraining loops. Designed for developers and beginners to machine learning who want to maintain production-ready models, this course requires no prior machine learning experience, though basic familiarity with C# is helpful. Start mastering the ML.NET model lifecycle today.