Building Custom Monitors for AutoML in ML.NET

When running automated machine learning (AutoML) experiments, understanding what happens behind the scenes during training is crucial for building reliable models. Standard logs often miss the specific metrics and events you need to evaluate performance efficiently. This text-based course guides you through creating custom runner monitors to track, intercept, and log key events during AutoML runs. You will gain complete visibility into your machine learning pipeline, enabling better debugging and model selection. What you'll learn: - Understand the core concepts of AutoML pipelines and experiment lifecycles in ML.NET. - Implement custom runner monitors using standard .NET interfaces to capture real-time training events. - Track metrics, trial completions, and optimization progress during active search phases. - Integrate modern .NET logging abstractions to output experiment data cleanly. - Handle errors and edge cases during long-running automated training processes. - Analyze monitored data to make informed decisions about model deployment. You will start with the foundational concepts of AutoML and the ML.NET framework before moving step-by-step through writing and registering your first custom monitor. This course is designed for .NET developers and aspiring data engineers who want to monitor machine learning workflows, requiring only a basic understanding of C# and no prior machine learning experience. Start reading today to take full control of your automated machine learning experiments.