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Automated machine learning (AutoML) automates the process of applying machine learning to data. Given a dataset, you can run AutoML to iterate over different data transformations, machine learning algorithms, and hyperparameters to select the best model.
Note
This article refers to the ML.NET AutoML API, which is currently in preview. Material is subject to change.
In general, the workflow to train machine learning models is as follows:
Preprocessing, training, and evaluation are an experimental and iterative process that requires multiple trials until you achieve satisfactory results. Because these tasks tend to be repetitive, AutoML can help automate these steps. In addition to automation, optimization techniques are used during the training and evaluation process to find and select algorithms and hyperparameters.
Whether you're just getting started with machine learning or you're an experienced user, AutoML provides solutions for automating the model development process.
It's recommended for beginners to start with the defaults provided by the high-level experiment API. For more experienced users looking for customization options, use the sweepable estimator, sweepable pipeline, search space, trial runner, and tuner components.
For more information on getting started with the AutoML API, see the How to use the ML.NET Automated Machine Learning (AutoML) API guide.
AutoML provides preconfigured defaults for the following tasks:
For other tasks, you can build your own trial runner to enable those scenarios. For more information, see the How to use the ML.NET Automated Machine Learning (AutoML) API guide.
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Events
Mar 17, 11 PM - Mar 21, 11 PM
Join the meetup series to build scalable AI solutions based on real-world use cases with fellow developers and experts.
Register nowTraining
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