analyzing-mlflow-trace

To debug an MLflow trace, you analyze the trace's spans, inputs, outputs, and assessments. Inspecting the full trace JSON allows you to identify status errors and form hypotheses about the root cause of the failure.

Yes, you can analyze MLflow trace spans to identify performance bottlenecks. By performing a timing analysis on the trace structure, you can pinpoint which specific spans are causing delays in the execution pipeline.

You investigate MLflow trace quality issues by examining span inputs, outputs, and assessments. This analysis helps identify mismatches between retrieved context and user intent, guiding your understanding of quality degradation.

The best way to understand a complex MLflow trace is to perform a structure walkthrough. Analyzing the trace JSON helps you interpret fields like info, data.spans, status, and rationale to map out the execution flow.

Yes, you need the full trace JSON to analyze your MLflow trace. Accessing the complete trace data, preferably exported via mlflow traces get, provides the necessary fields including spans, inputs, and outputs for accurate analysis.