Building Anomaly Detection for Feature Flags

Anomaly detection is critical for safe feature deployments. This guide covers how to build robust detection systems that catch problems before they impact users.

Why Anomaly Detection Matters

Without proper detection: - Issues go unnoticed for hours - Manual monitoring is error-prone - Alert fatigue leads to ignored warnings

Statistical Approaches

We use multiple detection methods: - **Z-score analysis**: Detects values outside normal distribution - **Moving averages**: Identifies trend deviations - **Seasonal decomposition**: Accounts for time-based patterns

Machine Learning Models

For complex patterns, ML models provide: - Multi-dimensional anomaly detection - Adaptive thresholds based on context - Correlation detection across metrics

Implementation Steps

1. 1. Define your key metrics (error rates, latency, conversion)
2. 2. Establish baseline patterns
3. 3. Configure detection sensitivity
4. 4. Set up automated response triggers

Best Practices

• Start with simple statistical methods
• Add ML for edge cases and complex patterns
• Always have human oversight for critical decisions