AutoML Overview
Explore the fundamentals of AutoML and its role in automating key machine learning pipeline steps such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation. Learn how ML.NET’s AutoML functionality simplifies building and deploying models, enabling developers to focus on defining problems and collecting data while the system handles the rest.
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AutoML, short for automated machine learning, is a set of techniques and tools that aim to automate various aspects of the ML pipeline, making it easier and faster for individuals without extensive ML expertise to build and deploy ML models. AutoML systems automate tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation.
AutoML aims to reduce the time and effort required for developing ML models by automating repetitive and time-consuming tasks. It democratizes ML by enabling nonexperts to leverage the power of ML in their respective domains, allowing them to focus on domain-specific problems rather than the intricacies of model building and optimization.