Registering and deploying a model using Model Registry
The Model Registry is the core enabler for MLOps, or DevOps for machine learning.
The Model Registry stores and manages machine learning models and associated metadata, such as the model's version, dependencies, and performance. The registry enables MLOps and facilitates the development, deployment, and maintenance of machine learning models in a production environment.
Model Registry includes functionality for the following tasks:
- Storing and organizing different versions of a machine learning model and its associated metadata.
- Tracking the lineage of a model, including who created it, when it was created, and any changes made to it over time.
- Managing dependencies between models and other assets, such as data sets and code.
- Providing APIs for accessing and deploying models, as well as for querying and searching the registry.
- Integrating with CI/CD pipelines and other tools used in the MLOps workflow.
Model registries help organizations improve the quality and reliability of their machine learning models by providing a centralized location for storing and managing models, as well as enabling traceability and reproducibility of model development. They also make deploying and managing models in a production environment easier by providing a single source for model versions and dependencies.
The Model Registry integrates MLFlow and maintains compatibility with the open source ecosystem.