MLflow Tracing Instrumentation Guide

Language-Specific Guides

Based on the user's project, load the appropriate guide:

• Python projects: Read references/python.md
• TypeScript/JavaScript projects: Read references/typescript.md

If unclear, check for package.json (TypeScript) or requirements.txt/pyproject.toml (Python) in the project.

Databricks: verify auth and use Unity Catalog trace storage by default

When the target is Databricks, read references/databricks.md before editing code and configure a UnityCatalog trace location. Calling only mlflow.set_tracking_uri("databricks") and mlflow.set_experiment(...) without a trace location uses legacy workspace experiment storage; that does not satisfy a request to send traces to Databricks.

Inspect existing project or environment configuration for candidate destinations and the optional table prefix. Before binding an experiment or provisioning UC resources, follow the schema-selection workflow in references/databricks.md: ask the user to choose an existing schema or create a new one unless they have already explicitly chosen the destination. Never select an arbitrary accessible schema. Discover SQL warehouses through the authenticated workspace and select any warehouse the user can access; do not ask the user to choose among usable warehouses. Ask for a warehouse only when no usable candidate can be discovered. Do not silently fall back to legacy workspace trace storage. Use legacy storage only when the user explicitly requests it.