Model Management

ClearML is an open-source platform for managing machine learning experiments, pipelines, and models.

A Model Base is a centralized repository for storing, managing, and versioning AI models.

The model lifecycle refers to the stages a machine learning model goes through from development to deployment and maintenance.

Model Lifecycle Management (MLM) is the process of overseeing AI model development, deployment, and maintenance.

Model Meta-Data refers to information that describes the characteristics of AI models.

Model Migration refers to the process of transferring machine learning models between environments or platforms.

A Model Registry is a central repository for managing, storing, and versioning machine learning models.

A model state represents the current configuration and parameters of an AI model during training or inference.

Model versioning is the practice of managing and tracking different iterations of machine learning models.