ML.NET AutoML: Configuring Custom Machine Learning Pipelines

Building machine learning models from scratch can be incredibly time-consuming, but automated machine learning simplifies the process. To get the best results, you need to know how to control and customize these automated pipelines rather than relying on default settings. This text-only course guides you through configuring AutoML experiments using ML.NET, giving you full control over how your models are trained and optimized. You will transition from running basic automated runs to strategically guiding the training process to match your specific dataset and performance goals. What you'll learn: Understand the foundational concepts of AutoML and how ML.NET pipelines automate model selection; Configure custom search spaces and adjust input data formats to optimize training efficiency; Exclude specific machine learning algorithms to focus resources on the most promising models; Set custom hyperparameter tuners and experiment duration limits to control execution; Apply modern model evaluation strategies to ensure high-quality, production-ready predictions; Implement basic model versioning and export practices for seamless .NET integration. The course starts with essential terminology and foundational pipeline concepts before moving into hands-on configuration techniques. You will explore structured text explanations and clear C# code snippets designed to show you exactly how to customize every stage of the AutoML lifecycle. This course is designed for developers and aspiring data engineers who are new to machine learning. No prior ML experience is required, though a basic understanding of C# is recommended. Start reading today to take control of your automated machine learning workflows in .NET.