1

Model Performance & Production Quality

Quality, accuracy, and reliability of models in production. Monitoring and degradation management.

Exceeds

Models in production meet or exceed accuracy targets. Degradation is detected and addressed before business impact.

Meets

Models perform adequately in production. Monitoring exists. Significant degradation is addressed.

Below

Models degrade without detection. Production reliability is a recurring issue.

Example review phrases

  • "Recommendation model now drives 23% of upsell revenue—and they built the monitoring to prove it."
  • "Implemented model drift detection that caught a data quality issue 11 days before it would have impacted user experience."
2

Business Impact

Whether ML work moves a business metric, not just improves a technical benchmark.

Exceeds

ML initiatives are directly tied to revenue, retention, or product quality metrics with measurable results.

Meets

ML work improves product functionality. Business impact is visible though not always precisely measured.

Below

ML work is technically interesting but has limited measurable business impact.

Example review phrases

  • "Churn prediction model flagged 87 at-risk accounts in Q2—CS followed up and retained $340K in ARR."
3

ML Engineering Rigor

Quality of ML pipelines, reproducibility, and engineering best practices applied to ML systems.

Exceeds

Experiments are reproducible. Feature pipelines are reliable. ML systems are treated as production software.

Meets

Experiments are logged and mostly reproducible. Pipelines are functional.

Below

Experiments are not reproducible. Pipelines are fragile. ML systems require significant manual operation.

Example review phrases

  • "Every experiment is fully reproducible—this is rare in ML teams of this size and has saved hours of debugging."
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Where do these examples come from in real reviews?

Most managers write performance reviews from memory—limited to what they personally observed. Confirm surfaces behavioral evidence from across the organization: who relied on this person, what they drove, how their impact extended beyond their direct manager's line of sight. Reviews written with Confirm's data are more accurate, more defensible, and faster to write.

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