What is AutoML?
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset, feature engineering, model selection, deployment and monitoring the solution. More specifically, AutoML automates the selection, composition and parameterization of machine learning models. Automating the machine learning process makes it more user-friendly and often provides faster, more accurate outputs than hand-coded algorithms.
AutoML helps to make machine learning less of a black box by making it more accessible. This process automates parts of the machine learning process that apply the algorithm to real-world scenarios. A human performing this task would need an understanding of the algorithm's internal logic and how it relates to the real-world scenarios. It learns about learning and makes choices that would be too time-consuming or resource-intensive for humans to do with efficiency at scale. Fine-tuning the end-to-end machine learning process - or machine learning pipeline - through meta learning has been made possible by AutoML. On a wider scale, AutoML also represents a step towards general AI.