Model Registry

AI Glossary

A model registry is your company’s central catalog for tracking every version of an AI model, what data it was trained on, and where it’s running right now.

What it really means

If you’ve ever managed a project with multiple file versions—say, a marketing brochure that went through five rounds of edits—you know how easy it is to lose track of which one is the final version. A model registry solves that same problem for AI models.

When I work with clients here in Central Florida, I describe a model registry as a cross between a library catalog and a version control system. Every time you train a new version of a model—maybe you add more customer data or tweak the algorithm—you “check it in” to the registry. The registry records what changed, when it changed, who changed it, and what data was used. It also keeps a running log of where each version is deployed: in production, in testing, or archived.

Think of it as a single source of truth. Instead of hunting through shared drives, Slack messages, or someone’s local folder called “final_final_v3,” you open the registry and see exactly what’s running and why. It’s a practical tool for keeping your AI operations organized and repeatable.

Where it shows up

Model registries are built into most major AI platforms. You’ll find them in cloud services like AWS SageMaker, Azure Machine Learning, and Google Vertex AI. Open-source tools like MLflow and Kubeflow also include registry features. If you’re using a platform to train or deploy models, chances are there’s a registry module already available.

In practice, the registry is usually a web-based dashboard or a set of API endpoints. Your data science team interacts with it when they push a new model version, and your operations team checks it when they need to update a production system. It’s not something most business owners will log into daily, but it’s the backbone that keeps your AI from turning into a messy pile of files.

Common SMB use cases

For small and mid-market businesses, a model registry becomes valuable once you have more than one model in play. Here are a few examples I’ve seen in Central Florida:

• An HVAC company in Maitland uses a model to predict when residential AC units are likely to fail. They train a new version every quarter with updated service records. The registry lets them roll back to a previous version if the new one starts making bad predictions.
• A dental practice in Winter Park runs a model that helps schedule appointments based on patient history and no-show patterns. When they add a new insurance plan, they retrain the model. The registry keeps the old version live while the new one is tested, then swaps them with zero downtime.
• A law firm in downtown Orlando uses a model to review contract clauses. They have different versions for different practice areas—real estate, family law, litigation. The registry tracks which version is appropriate for each case type and ensures the right one is used.
• A restaurant group in Lake Nona uses a model to forecast ingredient orders based on reservation data and weather forecasts. The registry helps them compare how each version performed during holiday rushes versus slow weeks.

In each case, the registry isn’t flashy. It’s just a reliable way to answer the question, “What model is running right now, and why?”

Pitfalls (what gets oversold)

The biggest oversell I hear is that a model registry will “automatically” solve all your deployment problems. It won’t. A registry is a record-keeping tool, not a deployment pipeline. You still need to set up the infrastructure to actually run the model—whether that’s a cloud server, an on-premise machine, or an edge device at a job site.

Another common pitfall is treating the registry as an afterthought. I’ve seen teams train a dozen models, then try to retroactively fill in the registry with notes like “trained on data from last year.” That defeats the purpose. The registry is only useful if you update it consistently, right when you create or change a model.

There’s also the risk of overcomplicating it. Some vendors pitch registries with complex governance features—approval workflows, audit trails, automated rollbacks. For most SMBs, that’s overkill. Start simple: a spreadsheet or a basic tool that logs model name, version number, training date, training data source, and deployment location. You can always upgrade later.

Finally, don’t confuse a model registry with a data catalog. The registry tracks the model itself. A data catalog tracks the data used to train it. They work together, but they’re different tools. Mixing them up leads to confusion about what you’re actually tracking.

Related terms

• Model versioning — The practice of assigning unique identifiers to different iterations of a model. A registry is the system that stores those versions.
• Model deployment — The process of putting a trained model into a production environment where it can make predictions. The registry tracks which version is deployed where.
• MLOps — The broader set of practices for managing machine learning models in production, including monitoring, retraining, and governance. A registry is one component of MLOps.
• Data lineage — The record of where your training data came from and how it was transformed. A good registry links each model version to its data lineage.
• Experiment tracking — A related tool that logs all the training runs and hyperparameter settings you tried before settling on a final model. Experiment tracking feeds into the registry when you promote a model to production.