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Model Deployment

Model deployment is the process of making a trained causal model available for use in other systems, either through the REST API or supported client libraries.

As a cloud-native platform, CausaDB models are designed to be deployment-ready out of the box. This means that once a model is trained, it can be used immediately in your applications.

Once a model has been created, defined, and trained, as below (in Python):

model = client.create_model("my-new-model")

It can be accessed by name in deployment. This is usually in a new session, or in a different application. To access a model by name in Python, use the get_model method:

model = client.get_model("my-new-model")

CausaDB will soon support model versioning, allowing you to keep track of changes to your model over time. This is useful for auditing, debugging, and for managing multiple versions of a model in production.