The machine learning (ML) life cycle refers to the stages in building, deploying, and maintaining a machine learning model. The problem that the ML model will solve needs to be clearly defined and understood. This includes identifying the type of data that will be used, the desired outcome, and the metrics that will be used to evaluate the model’s performance.

The ML life cycle can consist of but is not limited to the following stages: data collection, training unlabeled data, labeling, and model selection, evaluation, deployment, monitoring, and maintenance. The goal of the ML lifecycle is to develop and deploy machine learning models that are accurate, reliable, and can scale to deliver value to businesses or society.