name: databricks-ml-serving-ops description: Manage Databricks ML experiments, model registry, and serving endpoints via CLI microsoft_capability_family: "Azure / Databricks"

Databricks ML & Serving Ops Skill

Purpose

Manage Databricks ML experiments, feature engineering, model registry, and serving endpoints via the Databricks CLI (v0.205+).

Owner

databricks-cli-operator

Preconditions

• Databricks CLI v0.205+ installed
• Authentication configured with MLflow and serving permissions
• Unity Catalog enabled for model registry (recommended)

Covered Operations

Experiments (MLflow)

• databricks experiments list — list experiments
• databricks experiments get --experiment-id <id> — get experiment details
• databricks experiments create --name <name> — create experiment
• databricks experiments delete --experiment-id <id> — delete experiment (soft delete)

Feature Engineering

• databricks feature-engineering list-tables — list feature tables (Unity Catalog)
• Feature table operations are primarily API/SDK-driven; CLI provides listing and metadata

Model Registry (Unity Catalog)

• databricks registered-models list — list registered models
• databricks registered-models get --full-name <catalog.schema.model> — get model details
• databricks registered-models create --name <name> --catalog-name <cat> --schema-name <schema> — register model
• databricks registered-models delete --full-name <catalog.schema.model> — delete model (destructive)
• databricks model-versions list --full-name <catalog.schema.model> — list model versions
• databricks model-versions get --full-name <catalog.schema.model> --version <v> — get version details

Serving Endpoints

• databricks serving-endpoints list — list serving endpoints
• databricks serving-endpoints get --name <name> — get endpoint details
• databricks serving-endpoints create --json <spec> — create serving endpoint
• databricks serving-endpoints update-config --name <name> --json <spec> — update endpoint config
• databricks serving-endpoints delete --name <name> — delete endpoint (destructive)
• databricks serving-endpoints query --name <name> --json <input> — query endpoint for inference

Disallowed Operations

• Direct model training (use Jobs/notebooks, not CLI)
• Experiment run creation (use MLflow SDK in notebooks/jobs)
• Workspace artifact management (use databricks-workspace-ops)

Output Contract

• All commands use --output json
• Experiments return experiment_id, name, lifecycle_stage
• Models return full_name, creation_timestamp, comment
• Serving endpoints return name, state.ready, config.served_models

Verification

• After model register: registered-models get returns model metadata
• After endpoint create: poll serving-endpoints get until state.ready is READY
• After endpoint query: response contains predictions array