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Exercise

Aggregate model latency metrics from JSONL traces

Given a JSONL file of request traces, compute per-model latency averages, P95, and failure counts.

Data Transformation / medium / Step 1 of 16

Practice stage

Transform and summarize data safely

Hint

Think like an evaluation or trace pipeline: group clearly, preserve the data story, and make edge cases visible instead of clever.

Next drill
Summarize token usage by endpoint and model
Success criteria
  • - Handles missing or noisy records predictably
  • - Produces summary output that is easy to inspect
  • - Would be safe to rerun in a script workflow
Review checklist
  • - Do grouped metrics stay readable?
  • - Would malformed rows be debuggable?
  • - Did I choose names that explain the transformation?
Related lessons
Type safety and validation patterns for AI systemsEvaluation metrics for LLM outputs

Practice

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