# Databricks Model Serving

> Databricks Model Serving endpoint lifecycle and ops.

Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).

- Page: https://ss33.ai/skills/databricks/databricks-model-serving-9142700571
- Publisher: databricks
- Category: [Data & Analytics](https://ss33.ai/skills/category/data-and-analytics-m1r3i9) › [Machine learning & training](https://ss33.ai/skills/category/data-and-analytics-m1r3i9/machine-learning-and-training-d194fk)
- Source: https://github.com/databricks/databricks-agent-skills/blob/c570f88de43af3575bd33de6fef6186671afa9ba/plugins/databricks/claude/skills/databricks-model-serving/SKILL.md

## Skill

This skill's licence does not allow redistribution, so the store does not serve its text. Get it from the author: https://github.com/databricks/databricks-agent-skills/blob/c570f88de43af3575bd33de6fef6186671afa9ba/plugins/databricks/claude/skills/databricks-model-serving/SKILL.md