Build your model registry on top of Git
Reuse existing Git infrastructure for managing ML models together with code, data and metrics. With git as your single source of truth, use GitOps for model deployment.
Answer questions like,
- Which model version is currently in production?
- Who created this model version and when?
- Which code and dataset was used to train it?
- Who put this model into production?
- and more.
Manage your ML models through the entire lifecycle, from development to production.
Create versions and assign stages to models to build a timeline of model actions from experimentation to production.
Manage the lifecycle of each model as it moves through staging, production and other stages. See at a glance which model versions are in which stage.
Use the interface of your choice
Register, track, and version models with command line or from Iterative Studio.
Use the interface that you’re most comfortable with, whether you’re a data scientist who likes APIs, or a manager who prefers a web UI, or a DevOps engineer who works best with the command line interface (CLI) and uses GitOps principles for model deployment.
Build your model registry with Iterative's ecosystem of tools
Use Iterative tools for even more benefits, like linking versioned data with your code and models.
Gain additional capabilities around data versioning, pipeline management, experiment tracking, training automation, and more with Iterative tools including DVC and CML. Iterative's products are modular, so you can pick those components that extend what you already have. Because you work with your existing cloud and DevOps infrastructure, you can avoid vendor lock-in.