AutoML (Automated Machine Learning) refers to the use of artificial intelligence algorithms to automate the process of building, training, and deploying machine learning models. AutoML can automate tasks such as feature engineering, model selection, and hyperparameter tuning, reducing the need for manual intervention and expertise in machine learning.
AutoML platforms use a combination of algorithms and heuristics to explore the space of possible models and hyperparameters, selecting the best ones based on performance metrics such as accuracy, precision, and recall. AutoML has the potential to democratize machine learning by enabling non-experts to build and deploy machine learning models without requiring extensive knowledge of the underlying algorithms and techniques.
It has also shown promise in improving the efficiency and effectiveness of machine learning workflows in industries such as healthcare, finance, and manufacturing. While AutoML is still an emerging field, it has the potential to revolutionize the way we build and deploy machine learning models.