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Automatic machine learning (AutoML) is a general discipline that involves automating any part of the entire process of machine learning application. By working with various stages of the machine learning process, engineers develop solutions to expedite, enhance and automate parts of the machine learning pipeline.
Automatic machine learning is also known as automated machine learning.
Some automatic machine learning techniques and tools are geared toward expediting and automating data preparation – the aggregation of overall data from various sources. Other parts of this process are aimed at feature engineering – feature selection and feature extraction are a big part of how machine learning algorithms work. Automating these can further improve the machine learning design process.
Another part of automatic machine learning is hyperparameter optimization, which is done through various means. Engineers can use metaheuristics techniques like simulated annealing or other processes to make automatic machine learning happen. The bottom line is that automatic machine learning is a broad catch-all term for any technique or effort to automate any part of the machine learning “end to end” process.