This resource allows you to create MLflow models in Databricks.
Note 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.
resource "databricks_mlflow_model" "test" {
name = "My MLflow Model"
description = "My MLflow model description"
tags {
key = "key1"
value = "value1"
}
tags {
key = "key2"
value = "value2"
}
}
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.In addition to all arguments above, the following attributes are exported:
id
- ID of the MLflow model, the same as name
.The model resource can be imported using the name
terraform import databricks_mlflow_model.this <name>
The following resources are often used in the same context: