The primary function of a model registry is to centralize the storage of production-ready models. Developers can synchronize their efforts with other parties and stakeholders within the registry to manage the lifecycle of the company's models collectively. Trained models can be submitted to the model registry by a data scientist and undergo testing, verification, and operation.
Components of a Model Registry are:
- Central Storage: All types of models are preserved here for easy access by applications or services. Without such a warehouse for model artifacts, developers would have to store their work in scattered files within a central code repository. An open-source model registry eases the procedure by functioning as a core ML model repository.
- Unified Asset Management: The model registry acts as a collaborative platform for ML teams to work with and share models. An emphasis is on bridging experimental and production stages and providing a unified UI for model collaboration and consumption interface.
The Importance of Model Registration
Without a model registry, machine learning engineers may resort to shortcuts or make costly mistakes, such as:
- Incorrectly labeled model artifacts: Identifying the source of each artifact can be challenging. Miscommunication or loss of data can occur if the information is disseminated via email or chat.
- Data loss or deletion: Occurs when teams fail to record the usage history of specific datasets.
- Lost or unidentified source code versions: Not taking preventive measures may lead to the loss of the original code.
- Unrecorded model performance: The absence of performance data can hinder meaningful comparisons between model iterations.
Working of an ML Model Registry
Each model in the registry is assigned a unique identifier. Most registry tools also offer a feature for tracking multiple versions of the same model. Data science and ML teams can confidently compare and deploy models using the model ID and version.
Registry tools also offer storage options for parameters or metrics, making model comparisons more straightforward.
Typically, model registries comprise the following:
- Object storage: This is for storing model artifacts and large binary files.
- Structured database: Used for storing model metadata.
- Graphical user interface: Allows examination and comparison of models.
- Programmatic API: Enables retrieval of model artifacts and information via a model ID.
Model registry tools are vital for creating a robust MLOps infrastructure. They expedite research, development, and model deployment, making audits and governance essentially feasible.