Verta is singular in ML Infrastructure systems in that in provides both versioning and metadata management. While versioning allows safety and reproducibility for models, metadata provides ability for model governance, compliance, and visibility.

Looking for a tutorial? Head on to /tutorials/tutorials for a step-by-step walkthrough of Verta's versioning and metadata capabilities.


What is Versioning?

Versioning is the ability to track changes to an ML model over time and uniquely identify each state of an ML model by an ID so that one can navigate between different model states. In Verta, we version the constituent elements used to create a trained model (or inputs to a training process) as first-class entities in the system; specifically, we version the code, data, configuration, and the compute environment for a model. The output of a training process such as model weights or checkpoints are not part of the model version directly; instead these outputs are tracked via Artifacts and Metadata.

This distinction is shown in the picture below.

What is metadata?

Metadata is extra (or "meta") data about any of entities in the system such as Projects, Experiments, ExperimentRuns, and Models. Examples of metadata include:


Examples of




Tags, owner,

date creat



Tags, owner,

date creat



Metrics, AUC

curves, ta

gs, owner


Name, tags,

lifecycle s


How is Metadata different from Versioning?

Metadata, however extensive, does not enable you to go forward or backward in time to a specific state of a model. For instance, with metadata alone, you cannot go back to the exact state when a model was created three months back.

On the other hand, versioning is restrictive in what information is captured in a version. For instance, versions do not include extraneous artifacts like documentations and reports that are essential for data science activities. Only metadata can provide such information. As a result, the combination of versioning and metadata together is extremely powerful.

What is an Artifact?

An artifact is any binary or blob-like information. This may include the weights of a model, model checkpoints, charts produced during training, etc. In Verta, artifacts can be associated with a variety of entities including Projects and ExperimentRuns (most common).

Versioning and Metadata in Verta

Verta's ModelDB system provides model versioning as well as metadata capabilities for machine learning models. In order words, ModelDB enables data scientists to version their models and make them reproducible and enable them to associate rich metadata with these models.

Head over to the modeldb page for details about ModelDB capabilities.