ML metrics and stakeholder communication

Lesson 3 of 4 in Talking About AI at Work — Meetings, Stand-ups & Reviews · 22 minutes

ML engineers must translate metrics into business decisions for non-technical stakeholders. This lesson covers the patterns that work.

What you'll learn

Translate ML metrics for business audiences
Connect metrics to user impact
Discuss model performance changes
Manage executive expectations

From the lesson

ML metrics are precise but often opaque to non-ML stakeholders. Strong ML engineers translate metrics into business impact. This lesson covers the patterns.

1. The metric translation problem

ML metrics most engineers report: • Accuracy. • Precision. • Recall. • F1 score. • AUC. • MSE/RMSE. • Loss values.

These mean nothing to most stakeholders. Even technical-adjacent ones.

What stakeholders need: • Business impact. • User impact. • Cost impact. • Risk profile. • Comparison to current state.

The translation:

Don't say: • 'AUC improved from 0.87 to 0.91.'

Do say: • 'The new model catches 15% more fraud than current one.' • 'False positives dropped from 2,000 to 1,200 per day — fewer customers…