Learning from Common Mistakes in Anomaly Detection Systems Building anomaly detection systems looks straightforward in tutorials: load data, train a model, deploy, and watch it catch problems. Reality proves far messier. After reviewing dozens of failed deployments and interviewing teams who struggled with production systems, clear patterns emerge in where implementations go wrong. Understanding these pitfalls before you encounter them can save months of frustration and costly mistakes. Successful AI Anomaly Detection requires more than technical expertise—it demands awareness of subtle issues that emerge when theoretical models meet messy reality. Let's explore the most common mistakes and, more importantly, how to avoid them. Pitfall #1: Training on Contaminated Data The Problem Most teams assume their historical "normal" data is actually normal.…