Feature Request: Model Serving Endpoint support in Python DABs

[yarri-bryn-cla]

Describe the issue

Model Serving Endpoints can currently be defined in conventional Databricks Asset Bundles (YAML-based), but cannot be defined or managed using Python bundles (pyDABs). It is desirable to manage Mosaic AI Gateway & External Model endpoint configurations programmatically, as YAML-only configurations tend to get quite verbose.

The OpenAPI schema for model serving isn't immediately compatible for databricks-bundles packages' codegen because:

• Enum values containing dashes (e.g. amazon-bedrock) don't presently resolve to valid Python variables.
• Related model serving types being split across adjacent namespaces (model_serving_endpoints and serving), making the Python API inconsistent with existing usage patterns.

Configuration

This, or a derivative thereof (e.g. resources.add_model_serving_endpoint) are desired:

from databricks.bundles.model_serving_endpoints import ModelServingEndpoint

endpoint = ModelServingEndpoint(
    name="my-endpoint",
    config={
        "served_models": [...],
        "endpoint_tags": [...],
        "provider": "openai",
    },
)

Desired Behavior

• Model Serving Endpoints can be defined and managed in Python bundles
• Enums with dashes (e.g. amazon-bedrock) resolve correctly in Python
• Model serving–related types (e.g. EndpointTag) are accessible from a consistent namespace aligned with the existing API
• Users can configure Model Serving Endpoints programmatically (e.g. using OmegaConf) across environments
• Ability to manage Databricks Hosted LLM endpoints in DAB, e.g. AI Gateway config of Databricks GPT 5.2

Actual Behavior

• Model Serving Endpoints are only supported via YAML in conventional DABs
• Python bundles lack a ModelServingEndpoint resource
• Enum values with dashes cause issues in existing Python codegen if model serving namespaces are naively added
• Model serving types are fragmented across namespaces, making the API awkward to use without changes

Proposed solution (high level)

• Add a ModelServingEndpoint resource to Python bundles
• Update Python codegen to handle enum values containing dashes
• Provide cross-namespace re-exports for model serving types so related APIs are accessible under a consistent namespace
• Align Python bundle support with existing YAML-based DAB functionality
• Enable limited attribute management of Databricks Hosted LLM endpoints via DABs/PyDABs

Context

I initially submitted the below solution as a PR, and on request am redirecting to Issue. I currently manage Microsoft AI Foundry serving endpoints in our Azure Databricks workspaces using this implementation:

• PR: Add Model Serving Endpoints to databricks-bundles package #4340
• Branch: https://github.com/yarri-bryn-cla/cli/tree/add-python-model-serving