Automated Machine Learning — a set of cloud services that automate the end-to-end process of building, training, and deploying machine learning models without requiring deep ML expertise. Like having a self-driving car for data science, AutoML handles feature engineering, algorithm selection, hyperparameter tuning, and model evaluation automatically. AWS provides SageMaker Autopilot, Azure offers Azure AutoML, GCP has Vertex AI AutoML, and OCI offers OCI Data Science AutoML — all enabling business analysts and developers to build production-quality ML models from their data with minimal code.
A retail company uploads three years of sales data to an AutoML service and asks it to predict next quarter's demand. The service automatically tries dozens of algorithms, tunes thousands of parameter combinations, and delivers a highly accurate forecasting model in hours — work that would take a team of data scientists weeks to accomplish manually.
All four offerings automate key steps in model development (data prep guidance, algorithm selection, hyperparameter tuning, evaluation, and deployment options). AWS SageMaker Autopilot and Azure Automated ML are commonly used for tabular problems and integrate tightly with their broader ML platforms (pipelines, model registry, monitoring). Vertex AI AutoML provides managed training for tabular, vision, text, and some structured use cases within Vertex AI. OCI Data Science AutoML provides automated model training workflows within OCI’s Data Science service, typically focused on accelerating model experimentation and selection.