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Automatic machine learning (AutoML) is a general discipline that involves automating repetitive tasks in the machine learning (ML) process.
An important goal of AutoML is to reduce the need for highly-educated data scientists to build, train and maintain the machine learning algorithms an organization uses and make it easier for in-house software developers and line of business (LOB) professionals to use artificial intelligence (AI) to solve business problems.
Techopedia Explains Automatic 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.
