Machine learning is used in almost every sector, mostly in every industry such as Agriculture, finance, healthcare, and marketing. AutoML frameworks are a very important part of machine learning. An automatic machine learning framework can help a business scale its operations and maintain an efficient ML lifecycle. It also allows anyone to build machine learning models efficiently. Machine learning engineers and data scientists can accelerate ML development using AutoML frameworks. An automatic machine learning framework is an interface that allows developers, machine learning engineers, and data scientists to build and deploy their machine learning models efficiently.

Automated Machine Learning (AutoML) is a process of building a complete Machine Learning pipeline automatically, without (or with minimal) human help. The main goal of the AutoML framework was to find the best possible ML pipeline under the selected time budget. For its purpose, AutoML frameworks were training many different ML algorithms and tune their hyper-parameters. The improvements in the performance can be obtained by increasing the number of algorithms and checked hyper-parameters settings, which means longer computation time. The goal of AutoML analysis depends on the user.