Storage of ML Models
TileDB stores machine learning (ML) models as TileDB dense arrays and inherits all the critical functionalities of dense arrays by default. Versioning, time traveling, compression, encryption, and governance are available without any extra setup. In this way, TileDB offers an ML model registry, which will store your models alongside your data and code., without the need for any other model management tool.
Model registry
In general, an ML model registry offers the following advantages for organizations involved in deploying and managing ML models:
- Centralized model management
- Version control
- Collaboration and sharing
- Deployment automation
- Monitoring and governance
- Scalability and flexibility
Centralized model management
A model registry offers a centralized location to store, organize, and manage all deployed models. This centralized repository allows fast access, version control, and tracking of model artifacts, metadata, and performance metrics.
Version control
Versioning is crucial in ML model development to keep track of changes, experiments, and improvements over time. A model registry enables versioning of models, allowing users to compare different versions, rollback to earlier versions if needed, and ensure reproducibility of results.
Collaboration and sharing
With a model registry, data scientists and other stakeholders can collaborate efficiently by sharing models, experiments, and insights. It promotes collaboration by offering a common platform for teams to discuss, review, and iterate on models, fostering knowledge sharing and innovation.
Deployment automation
Integrating a model registry with deployment pipelines and automation tools streamlines the process of deploying models into production environments. By linking model artifacts with deployment configurations, organizations can automate deployment tasks, reduce manual errors, and ensure consistency across environments.
Governance and monitoring
A model registry includes governance and monitoring features to track model performance, detect anomalies, and ensures access control and compliance with regulatory requirements. It offers visibility into a model’s behavior in production, alerts stakeholders to potential issues, and helps preserve the reliability and integrity of deployed models.
Scalability and flexibility
As organizations scale their ML initiatives and deploy more models into production, a model registry offers scalability and flexibility to manage a growing number of models efficiently. It can accommodate diverse types of models, frameworks, and deployment environments, adapting to evolving business needs.
In general, a model registry serves as a foundational component of the ML lifecycle, allowing collaboration, version control, deployment automation, and governance. Organizations that use a model registry can get the most value from their machine learning investments.