mlflow_model.md

subcategory
MLflow

databricks_mlflow_model Resource

This resource allows you to create MLflow models in Databricks.

-> This documentation covers the Workspace Model Registry. Databricks recommends using Models in Unity Catalog. Models in Unity Catalog provides centralized model governance, cross-workspace access, lineage, and deployment.

Example Usage

resource "databricks_mlflow_model" "test" {
  name = "My MLflow Model"

  description = "My MLflow model description"

  tags {
    key   = "key1"
    value = "value1"
  }
  tags {
    key   = "key2"
    value = "value2"
  }
}

Argument Reference

The following arguments are supported:

• name - (Required) Name of MLflow model. Change of name triggers new resource.
• description - The description of the MLflow model.
• tags - Tags for the MLflow model.

Attribute Reference

In addition to all arguments above, the following attributes are exported:

• id - ID of the MLflow model, the same as name.

Import

The model resource can be imported using the name

terraform import databricks_mlflow_model.this <name>

Access Control

• databricks_permissions can control which groups or individual users can Read, Edit, Manage Staging Versions, Manage Production Versions, and Manage individual models.

Related Resources

The following resources are often used in the same context:

• databricks_registered_model to create Models in Unity Catalog in Databricks.
• End to end workspace management guide.
• databricks_model_serving to serve this model on a Databricks serving endpoint.
• databricks_directory to manage directories in Databricks Workspace.
• databricks_mlflow_experiment to manage MLflow experiments in Databricks.
• databricks_notebook to manage Databricks Notebooks.
• databricks_notebook data to export a notebook from Databricks Workspace.
• databricks_repo to manage Databricks Repos.