Deploy Scalable, Production-Ready ML Models with Databricks and ZenML
Integrate Databricks Model Serving with ZenML to deploy and serve AI models effortlessly. This integration provides a unified interface to deploy, govern, and query models, leveraging Databricks' managed infrastructure for scalability and enterprise security.
from zenml.integrations.databricks.steps.databricks_deployer import databricks_model_deployer_step
@step(enable_cache=False)
def deployment_deploy() -> (
Annotated[
Optional[DatabricksDeploymentService],
ArtifactConfig(
name="databricks_deployment", is_deployment_artifact=True
),
]
):
# deploy predictor service
zenml_client = Client()
model_deployer = zenml_client.active_stack.model_deployer
databricks_deployment_config = DatabricksDeploymentConfig(
model_name=model.name,
model_version=model.run_metadata["model_registry_version"].value,
workload_size="Small",
workload_type="CPU",
scale_to_zero_enabled=True,
endpoint_secret_name="databricks_token",
)
deployment_service = model_deployer.deploy_model(
config=databricks_deployment_config,
service_type=DatabricksDeploymentService.SERVICE_TYPE,
timeout=1200,
)
logger.info(
f"The deployed service info: {model_deployer.get_model_server_info(deployment_service)}"
)
return deployment_service
@pipeline(on_failure=notify_on_failure)
def databricks_deploy_pipeline():
deployment_deploy()
notify_on_success(after=["deployment_deploy"])
databricks_deploy_pipeline().run()
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