Building Custom AutoML Monitors for ML.NET Pipelines

When automated machine learning runs multiple trials to find the best model, keeping track of the training progress and metrics is crucial. Without custom monitoring, you risk losing visibility into how your pipelines are performing during the optimization process. This text-based course guides you through the process of setting up, customizing, and executing monitors within the ML.NET AutoML framework. By completing this course, you will transform your approach to model training by learning to capture vital trial data, log the top-performing models, and apply modern MLOps tracking principles to your local development workflow. What you'll learn: - Understand the fundamental architecture of ML.NET pipelines and how AutoML operates. - Configure custom monitor classes to hook into the AutoML trial lifecycle. - Log critical training metrics and track the best-performing models in real time. - Apply modern logging abstractions to organize and store trial history. - Implement basic MLOps monitoring patterns to ensure model transparency. - Practice writing custom evaluation logic to filter trials based on specific performance thresholds. You will start with foundational concepts of automated machine learning before progressing to step-by-step implementations of custom monitor classes. Through clear written explanations and structured code snippets, you will learn how to integrate these monitors directly into your training pipelines. This course is designed for software developers and beginner machine learning engineers who have a basic familiarity with C# and want to gain control over their automated training workflows. No advanced data science background is required. Start reading today to bring deep visibility and robust tracking to your ML.NET projects.