What is Error Rate Monitoring?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Track prediction failures where the model fails to return a result, validation errors from malformed input requests, timeout errors where inference exceeds SLO limits, infrastructure errors from service unavailability, and data errors where input features are missing or corrupt. Additionally track silent errors where the model returns results but confidence scores indicate unreliability. Each error type needs different response procedures and different alert thresholds.

Set critical alerts at 2x baseline error rate sustained for 5 minutes. Set warning alerts at 1.5x baseline sustained for 15 minutes. Use relative thresholds rather than absolute percentages since normal error rates vary by model and endpoint. For customer-facing systems, target total error rate below 0.1%. Calculate error rate over rolling windows of 5-15 minutes rather than instantaneously to avoid alert noise from momentary spikes. Review and adjust thresholds quarterly based on observed false alert rates.

Transient errors occur randomly and resolve without intervention, like network timeouts or temporary resource contention. Systematic failures show consistent patterns like all requests from a specific client failing or error rates increasing monotonically. Use error correlation analysis to detect systematic patterns: if errors cluster by time, endpoint, or input characteristics, the failure is likely systematic. Automated classification of transient versus systematic errors helps prioritize incident response and prevents unnecessary escalation of transient issues.

Track prediction failures where the model fails to return a result, validation errors from malformed input requests, timeout errors where inference exceeds SLO limits, infrastructure errors from service unavailability, and data errors where input features are missing or corrupt. Additionally track silent errors where the model returns results but confidence scores indicate unreliability. Each error type needs different response procedures and different alert thresholds.

Set critical alerts at 2x baseline error rate sustained for 5 minutes. Set warning alerts at 1.5x baseline sustained for 15 minutes. Use relative thresholds rather than absolute percentages since normal error rates vary by model and endpoint. For customer-facing systems, target total error rate below 0.1%. Calculate error rate over rolling windows of 5-15 minutes rather than instantaneously to avoid alert noise from momentary spikes. Review and adjust thresholds quarterly based on observed false alert rates.

Transient errors occur randomly and resolve without intervention, like network timeouts or temporary resource contention. Systematic failures show consistent patterns like all requests from a specific client failing or error rates increasing monotonically. Use error correlation analysis to detect systematic patterns: if errors cluster by time, endpoint, or input characteristics, the failure is likely systematic. Automated classification of transient versus systematic errors helps prioritize incident response and prevents unnecessary escalation of transient issues.

Track prediction failures where the model fails to return a result, validation errors from malformed input requests, timeout errors where inference exceeds SLO limits, infrastructure errors from service unavailability, and data errors where input features are missing or corrupt. Additionally track silent errors where the model returns results but confidence scores indicate unreliability. Each error type needs different response procedures and different alert thresholds.

Set critical alerts at 2x baseline error rate sustained for 5 minutes. Set warning alerts at 1.5x baseline sustained for 15 minutes. Use relative thresholds rather than absolute percentages since normal error rates vary by model and endpoint. For customer-facing systems, target total error rate below 0.1%. Calculate error rate over rolling windows of 5-15 minutes rather than instantaneously to avoid alert noise from momentary spikes. Review and adjust thresholds quarterly based on observed false alert rates.

Transient errors occur randomly and resolve without intervention, like network timeouts or temporary resource contention. Systematic failures show consistent patterns like all requests from a specific client failing or error rates increasing monotonically. Use error correlation analysis to detect systematic patterns: if errors cluster by time, endpoint, or input characteristics, the failure is likely systematic. Automated classification of transient versus systematic errors helps prioritize incident response and prevents unnecessary escalation of transient issues.