Databricks
1. What the integration does
When you run MLflow inside Databricks, every experiment emits OpenTelemetry‑compatible traces. By directing those traces to Patronus AI’s managed OTel Collector, you gain real‑time visibility, evaluation metrics, and alerting—with zero extra code beyond three environment variables.
2. One-minute setup (3 env-vars)
Databricks tip: Set these under Cluster → Configuration → Environment Variables or inject them at job runtime via spark_env_vars.
3. Install the OTLP exporter (once per cluster)
Databricks caches the wheel, so subsequent jobs start fast.
4. Smoke-test the connection (optional)
Open Patronus AI → Traces and confirm the span appears under the service name you set.
5. How it works under the hood
- MLflow generates OTel traces as experiments run.
- The OTLP exporter sends those traces to Patronus’s public endpoint: "https://otel.patronus.ai:4317".
- The Collector authenticates with your API key, tags data with pat-project-name (if provided), and forwards it to Patronus ingestion.
- Patronus AI renders dashboards, stores history, and triggers alerts—no on‑prem infra required.
6. Advanced tuning (optional)
Common tweaksNote on mlflow.autolog() behavior
MLflow ≥ 2.12 no longer logs underlying model inputs/outputs when you call mlflow.autolog(). If your Databricks notebooks depend on those logs, enable the OpenAI‑specific helper instead:
A WARN line is printed to the console when this change is detected, but the snippet above is the quickest fix.
All OpenTelemetry configuration options are supported. See the OpenTelemetry spec for the full list.