# Databricks Model Serving

> 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 AI Gateway rate limits; 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: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).

## Facts
- Page: https://tashan.sh/capability/skill-databricks-databricks-model-serving
- tashan id: skill:databricks/databricks-model-serving
- Source: https://github.com/databricks/databricks-agent-skills
- Type: skill
- Category: ai
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: not measured
- Freshness: not measured
- Evidence coverage: not measured
- Health: not measured
- Instruction depth: not yet graded
- Official: no

## Install

```sh
cp -r databricks-model-serving ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-04 by tashan (https://tashan.sh) from public evidence. Scorer s5.