MLflow Tracing - GenAI observability
MLflow Tracing is a powerful feature that provides end-to-end observability for GenAI applications, including complex agent-based systems. It records inputs, outputs, intermediate steps, and metadata to give you a complete picture of how your app behaves.
Tracing allows you to:
- Debug and understand your application
- Monitor performance and optimize cost
- Monitor production applications
- Evaluate and enhance application quality
- Ensure auditability and compliance
- Integrate tracing with many popular third-party frameworks
- Use natural language with Databricks Assistant to analyze, debug, and explore trace data
Next steps
- 10-minute tracing demo - Get started with tracing in Databricks.
- Instrument your app - Choose between automatic and manual tracing approaches
- Databricks Assistant for agent observability and evaluation - Use the Databricks Assistant to analyze traces, debug errors, and explore evaluation data using natural language.