AutoML
Definition of AutoML
AutoML is automated Machine Learning, a technique for automatically selecting and optimizing Machine Learning models.
What is AutoML used for?
AutoML (Automated Machine Learning) is a suite of machine-learning techniques used to automate the process of building and training predictive models. With AutoML, users can train powerful, accurate models with minimal effort and time compared to traditional manual development. AutoML applies data preprocessing, feature engineering, model selection, hyperparameter tuning and other methods that are traditionally done manually by data scientists. It has the potential to speed up the development cycle of a machine learning project significantly, thus making it more accessible for people who want to make use of machine learning but don’t have the expertise or time to devote to manual coding. By automating these core tasks, AutoML simplifies the process of developing sophisticated machine learning models while providing greater accuracy than typical manual approaches. Additionally, it enables non-experts such as business analysts or government officials to create complex predictive models without having to learn complex programming languages or mathematics. Ultimately, AutoML could be used in any scenario where predictive modeling is required and could potentially revolutionize how businesses and governments use AI technology.