A model registry is, at its core, a repository created specifically for machine learning models. The registry lets you more effectively and efficiently track your models in one place while they undergo training and deployment. Just as importantly, a registry imposes a consistent file naming system. Beyond the essential consolidation of the models, tracking aspects, and consistent file names, the ML model registry also lets you store related model-specific metadata and artifact data. When used properly, a model registry takes the place of a version control system and repository.
The primary function of a model registry is to provide unique identifiers and storage for each model. This streamlines retrieval for use by both yourself and other teams. Depending on the system you use, other functions include deployment automation and access control features. These functions can speed up certain tasks, but also improve transparency and accountability.
For example, you can see who deployed a model and when. If they rolled back a model to an earlier version, you can ask them why. You may discover that the new version provides less accurate results than the prior model, which can help expose flaws in the training data.
A typical ML model registry is compromised for a few key components, including:
A database, either structured or semi-structured
A user interface, either GUI, command line interface, or both
A programmatic API
These components allow access not only to the models for the data scientist but also provides easier access for the operations team.
A model registry offers a few key benefits for an organization. For the data scientist or scientists actually developing the models, they get a pre-built way of organizing and sharing models, metadata, and artifacts. For operations teams, the same system provides them with a graphical user interface that lets them access the same information in a relatively easy-to-use way.
The system also helps answer common questions that operations teams might ask, such as how a model was trained, what data was used, or which version of the model they’re using at present. Automation features save time on deployment, while access control features help keep records of who changed what and when.
Machine learning models can provide organizations with powerful insight and extremely useful data, but they shouldn’t exist in unintentional black boxes. A model registry takes the black box out of the equation by providing a system and place for tracking ML models. You get model storage, file name consistency, metadata storage, and even artifact storage. All of this helps keep your machine learning team and your operations team on the same page.
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