analyzing-mlflow-trace

To debug a failed MLflow trace, retrieve the payload using the trace ID via the CLI. Analyze the span tree and status codes to locate the fault, correlating parent and child spans to application steps for precise root-cause identification.

Interpreting MLflow assessments requires examining the rationale field to distinguish scorer errors from actual trace errors. An OK status may mask underlying issues, so focus on the assessment rationale and span status codes to understand execution quality.

Inspect MLflow span trees by correlating span names and parent-child structure to your application functions. This maps individual spans to specific tools, retrieval steps, or model calls, allowing you to narrow down the root cause of the failure.

Large MLflow trace payloads can result in empty or partial output during analysis. To avoid this, use recommended CLI handling techniques to manage payload size, ensuring the full trace JSON structure is processed for accurate debugging.

Yes, you can perform root-cause analysis on a single MLflow execution without extra dependencies. The Skill requires only the trace ID and analyzes the trace JSON structure, including span trees and status codes, to explain behavior and locate faults.