AutoML (Automated Machine Learning)

AutoML refers to the automated process of applying machine learning models to real-world problems, handling tasks from data preprocessing through model selection and optimisation with minimal human intervention.

AutoML platforms automate the end-to-end process of applying machine learning to real-world problems, making AI more accessible to non-experts. These systems automatically handle complex tasks such as feature engineering, algorithm selection, hyper parameter tuning, and model optimisation that traditionally required significant expertise.

The technology democratises machine learning by reducing the expertise needed to develop effective AI solutions. AutoML tools can test multiple algorithms and configurations simultaneously, often discovering optimal solutions that might be overlooked by human data scientists, whilst significantly reducing the time and resources required for model development.

Examples

• Google Cloud AutoML
• Azure Automated Machine Learning
• H2O.ai AutoML
• Amazon SageMaker Autopilot
• DataRobot