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 to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning, with the high degree of automation aiming to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning.
Automated machine learning can target various stages of the machine learning process, including data preparation and ingestion, column type detection, column intent detection, model selection, and hyperparameter tuning. AutoML automatically locates and uses the optimal type of machine learning algorithm for a given task, making it more user-friendly and often providing faster, more accurate outputs than hand-coded algorithms. Automated machine learning enables organizations to use the baked-in knowledge of data scientists without expending time and money to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.
AutoML provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning, and to post-process machine learning models. The goal of automated machine learning is to automate the time-consuming, iterative tasks of building a machine learning model, allowing data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
