Machine learning in automation testing produces data sets that approximate personal profile images and information such as age and weight. The data depends on machine learning algorithms trained and learned from current production data sets. These data sets are similar to production data and are suitable for software testing. Automated machine learning (AutoML) is a process that automatically performs many of the repetitive and time-consuming tasks involved in developing models.
It was developed to increase the productivity of data scientists, analysts, and developers and to make machine learning more accessible to those with less data experience. Automated machine learning automates the selection of different variables from a given data set that should be used in a model, as well as the algorithms necessary to create that model. It deals with the most mundane and repetitive tasks of machine learning, with the promise of accelerating the development process of AI and making technology more accessible.
