I review your deployed ML system and identify production risks before they become business problems.

Most ML systems are evaluated once at launch and never checked again. This audit finds the gaps that don't show up until something breaks in production — bad thresholds, silent data drift, missing monitoring, or evaluation metrics that look fine but hide a broken model.

You'll receive:

✅ Model evaluation & threshold review — whether your decision threshold aligns with your actual business objectives, and the trade-off between missed cases and false alarms

✅ Class imbalance & data quality assessment — surfaces whether your evaluation metrics are hiding poor real-world performance

✅ Deployment architecture review — API design, containerisation, and reliability of your serving layer

✅ Monitoring & drift detection assessment — whether you'd know if your model started failing silently

✅ Prioritized recommendations report — ranked by risk and effort, so you know exactly what to fix first

Delivered as a written report within 5 business days.