Custom AutoML Monitoring in ML.NET Solutions

Automated Machine Learning (AutoML) simplifies model training, but understanding how different trials perform in real-time is crucial for building production-ready applications. This text-based guide teaches you how to implement custom monitors to track, log, and evaluate AutoML trials as they run. You will transition from running black-box AutoML experiments to having full visibility into the training process. By learning how to hook into the ML.NET AutoML pipeline, you will be able to capture metrics, log progress using modern .NET logging abstractions, and make informed decisions about your machine learning pipelines. What you'll learn: - Understand the core architecture of the ML.NET AutoML engine and trial execution. - Create custom monitor classes by implementing the required ML.NET interfaces. - Capture real-time training metrics, including accuracy, loss, and training duration. - Integrate custom monitors with modern .NET logging frameworks for structured telemetry. - Handle trial completions, failures, and timeouts gracefully within your code. - Analyze trial outputs to select and save the best-performing machine learning model. The course begins with foundational concepts of AutoML and the life cycle of a training trial. You will then progress through step-by-step written explanations to implement, register, and run a custom monitor within a standard .NET application. This course is designed for .NET developers who are new to machine learning and want to gain deeper insights into their AutoML training processes. No prior machine learning experience is required, though basic familiarity with C# and .NET is recommended. Start reading today to take control of your automated machine learning pipelines in .NET.