When technology providers talk about ML model registries, they describe them as "team collaboration spaces" or "model repositories," but they are so much more. How can enterprises determine what they need and how they can truly benefit from an ML model registry?
A robust collaboration space around ML model lifecycle management ensures that users, from data scientists to ML engineers to MLOps engineers, can work as a team to build more mature AI systems and initiatives. Collaborating around models from experimentation mode to live models in production, teams can capture the granular details and artifacts for each model. In these more structured environments, roles and responsibilities are well defined to enable better collaboration, clear hand-offs, and accountability.
Some enterprises may be just getting started on their AI journeys. With no formal organizational structure, data scientists moonlight as ML engineers or IT professionals double as MLOps engineers. In these cases, the purpose of an ML model registry might be more about creating a system of record. That means that no matter who is doing the work, there is a rock-solid, trustworthy repository to house critical artifacts, model history, and other information associated with each model, no matter the user.
Organizations that have started to put a few models into production will want to scale them into a practice that benefits business. An ML model registry can provide flexibility to handle everything from collaboration to system of record to leveraging scale.
As part of our end-to-end Vian MLOps Platform, we provide an enterprise-wide model repository to manage and maintain all ML models in one place, regardless of which tools were used to build the models. Our platform is open and flexible while many other offerings can only import and register models created in certain proprietary tools.
As organizations accelerate the pace at which they deploy models, it becomes increasingly important that it is easy for users to track specific versions of the models that performed best, and to ensure the transformers used to train them are available to be packaged with the model. Experiment management becomes critical as in-production models begin to drift, and need to be quickly retrained and redeployed. A repository serves as a single source of truth to drive continuous operations.
Our platform allows extreme flexibility for enterprises regardless of organizational maturity by providing an open-first approach. Platform capabilities are delivered in modules, each wrapped as a service and accessible through the intuitive UI or directly with APIs.
As an organization embarks on its AI journey and thinks about ML model lifecycle management, it can ramp up small numbers of models into production to enterprise scale. Those already familiar with the process are provided the rich features, easy access to model data, and automated workflows that help a range of stakeholders easily participate in the process and flexibility to bring more models to production in a both centralized and decentralized approaches.
Need a better way to inventory and manage models regardless of the tools used to build them? With the Vian ML Model Registry, we provide a common registry to manage any model, enable collaboration and communication between disparate teams, and provide the detailed inventory, model lineage, reproducibility and traceability to build trust in the organization. Want to see a demo? Request one here.