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).
Works with: Claude Code (native) · Cursor, Codex CLI (manual)
native: this artifact type is that client's own format
Category: Other — see all ranked ›
Install (Claude Code):
cp -r databricks-model-serving ~/.claude/skills/- Adoption: 1 repos
Security audit
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source ↗ · skill:databricks/databricks-model-serving
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Measured 2026-08-03 · scorer s5 · how · something wrong here?