Model lifecycle describes the stages a machine learning model passes through from development to deployment, monitoring, updating, and retirement. It provides a process for managing performance and risks. Stages include preparing data, building and testing the model, validating results, deploying it, and monitoring behavior. Models may be updated when requirements change.
For example, an online retailer may retrain a recommendation model when customer behavior changes, keeping suggestions relevant. Managing the lifecycle helps detect problems, maintain performance, reduce risks, and keep models aligned with goals. It is used in fraud detection, forecasting, personalization, and automated decision-making.
