Can you recommend an anomaly detection monitor for alerting on model degradation in real time?

Yes — for real-time alerting on model degradation, I’d recommend using an anomaly detection monitor on model performance / prediction drift, with a strong preference for a tool that supports streaming metrics, baselines, and alert thresholds.

Good options

1. Evidently AI

   • Great for monitoring data drift, target drift, and performance degradation
   • Supports dashboards and alerts
   • Works well if you want something ML-specific and relatively easy to integrate

2. WhyLabs

   • Strong for production model monitoring
   • Built for real-time anomaly detection, drift, and data quality issues
   • Good if you want managed monitoring with alerting and observability

3. Arize AI

   • Very solid for model observability
   • Detects drift, prediction issues, and performance drops
   • Good for teams that want deeper analysis and root-cause investigation

4. NannyML

   • Useful when you don’t always have ground truth labels immediately
   • Estimates post-deployment performance and detects degradation
   • Great for delayed-label settings

What to monitor

For model degradation, I’d suggest alerting on:

• Feature drift
• Prediction distribution drift
• Confidence / uncertainty changes
• Error rate / latency
• Business KPI proxies
• Post-label performance when labels arrive

My practical recommendation

• If you want open-source + flexible: Evidently AI
• If you want managed production monitoring: WhyLabs or Arize
• If labels are delayed and you need degradation detection anyway: NannyML

Simple rule of thumb

If your goal is real-time alerting, use:

• a rolling baseline window
• population drift metrics like PSI / KS / KL divergence
• alert thresholds with smoothing to avoid noise
• separate monitors for features, predictions, and latency/errors

If you want, I can also recommend:

• a specific monitor setup for your stack,
• or compare Evidently vs WhyLabs vs Arize for your use case.