Automated Machine Learning (autoML) describes the methods and processes that automate a machine learning model’s iterative tasks. Building traditional machine learning models is a complex process that requires domain expertise, time, and significant resources. Automated machine learning helps make this process less complex by automating the more time-consuming parts of training and running machine learning models. Automated machine learning makes it so analysts, data scientists, and developers can build making learning models quicker, more efficiently, and at greater scale. When using autoML, all an individual needs to do is identify a problem it wants to use machine learning to solve, point the autoML solution to the data set it wants it to use to solve that problem, configure the autoML program to abide by certain parameters, and run the model. The automated machine learning system will test, train, and run the model and then provide you with solution to the problem you posed to it.