Update model serving endpoint

[pablobd]

Hi all,

I've been able to create a model serving endpoint through the api, following the docs, but then when trying to update the version, I get the following error:

'{"error_code":"RESOURCE_ALREADY_EXISTS","message":"Endpoint with name \'ml-project\' already exists."}'

Appreciate you help and insights.
Thanks

[Aaron12]

I was also searching for a solution to this problem when I came across your similar issue. This approach seems to work well. It either updates the config of the existing endpoint, or creates a new one if it doesn't exist.

# Define the endpoint config
data = {
    "name": endpoint_name,
    "config": {
        "served_models": [
            {
                "model_name": model_name,
                "model_version": model_version,
                "workload_size": "Small",
                "scale_to_zero_enabled": True,
                "environment_vars": {
                    "OPENAI_API_KEY": f"{{{{secrets/{secret_scope_name}/{secret_key_name}}}}}"
                }
            }
        ]
    }
}

# Get list of active endpoints
endpoint_list_response = requests.get(
    url=f"https://{db_host_url}/api/2.0/serving-endpoints",
    headers=headers
)

# Check if endpoint already exists
endpoints = endpoint_list_response.json().get("endpoints", [])
endpoint_exists = any(ep['name'] == endpoint_name for ep in endpoints)
print("Endpoint exists:", endpoint_exists)

# Update endpoint config if it exists
if endpoint_exists:
    update_response = requests.put(
        url=f"https://{db_host_url}/api/2.0/serving-endpoints/{endpoint_name}/config",
        json=data["config"],
        headers=headers
    )

    print("Update Response status:", update_response.status_code)
    print("Update Response text:", update_response.text, "\n")

# Create endpoint if it doesn't exist
else:
    create_response = requests.post(
        url=f"https://{db_host_url}/api/2.0/serving-endpoints",
        json=data,
        headers=headers
    )

    print("Create Response status:", create_response.status_code)
    print("Create Response text:", create_response.text, "\n")

[BR_DatabricksAI]

Folks, Alternate way you can also deploy the models in serving layer with different versions. Though I am using mLflow. 

You may also refer to the below link if it its helpful 

How to Quickly Deploy, Test & Manage ML Models as REST Endpoints with Databricks - The Databricks Bl...

Thanks.