A model registry is a centralized repository for managing and versioning machine learning models throughout their lifecycle. It serves as a single source of truth for model metadata, artifacts, and deployment information.

What is a model registry used for?

Model registries enable data science teams to track, version, and manage ML models efficiently. They facilitate collaboration, reproducibility, and governance in the model development process.

For instance, a fintech company using a model registry can easily track different versions of their credit scoring model, compare performance metrics, and quickly roll back to a previous version if issues arise in production.

What information gets stored in the model registry?

Model registries typically store:

Model artifacts (e.g., serialized model files)Metadata (e.g., model name, version, creator, creation date)Performance metricsHyperparametersDependencies and environment informationDeployment status and history

Consider a healthcare AI system. Its model registry might contain multiple versions of a diagnostic model, each with associated accuracy metrics, training datasets, and deployment records across various hospitals.

What is the difference between model registry and experiment tracking?

While often complementary, model registries and experiment tracking serve distinct purposes.

Experiment tracking focuses on the model development phase, capturing details of training runs, hyperparameter tuning, and intermediate results.Model registries, in contrast, deal with the lifecycle management of production-ready models. They're concerned with versioning, deployment, and governance of models that have graduated from the experimentation phase.

Think of experiment tracking as a scientist's lab notebook, while the model registry is more akin to a product catalog and version control system.

How does Shakudo's approach to model registries differ from traditional MLOps platforms?

Shakudo's model registry solution exemplifies our commitment to flexibility and interoperability. Unlike monolithic platforms that force you into their ecosystem, our model registry integrates seamlessly with your existing tools and workflows.

We provide a robust, cloud-agnostic model registry that can be deployed on your infrastructure, ensuring data sovereignty and eliminating vendor lock-in. This approach allows you to leverage best-of-breed tools for model development while maintaining a centralized system for model management and governance.

For example, a Shakudo client in the autonomous vehicle industry can use their preferred deep learning frameworks for model development, while relying on our model registry to manage model versions across their global testing facilities. This flexibility accelerates innovation without compromising on governance or scalability.