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Iterative Studio Model Registry Uses GitOps Approach To Solve Productivity Gap Between DevOps And MLOps

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Iterative has announced the launch of Iterative Studio Model Registry, the first machine learning model registry based on GitOps principles. The model registry gives organizations an interface to not only search and explore models but to manage them, moving various models across the ML lifecycle, from development to production and retirement.

Unlike existing solutions that are separate from software development tools and often not updated with the latest model information, Iterative takes the workflows and best practices of software development and applies them to model deployment, getting models into production faster. DevOps and MLOps teams collaborate by using the same tools and processes so production-ready models being passed downstream to CI/CD systems are all fully automated and transparent to all teams.

The model registry is made with fully modular components. So whether it’s a data scientist who prefers APIs, a manager who prefers a web user interface, or a DevOps engineer who works best with the command line interface (CLI), Iterative Studio Model Registry meets users where they are. This way, team members use the interface that they’re most comfortable with in order to create and collaborate on ML models quickly and seamlessly.

And for organizations in general, the model registry and various open-source components that simplify model deployment like MLEM, plug into their existing MLOps stack without any worries around vendor lock-in or compatibility.

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