40How do you detect outliers and anomalies, and which method do you choose?▼mediumAmazonGoogleMicrosoft1 replies◆ premiumOutlier detection shows up in data cleaning, fraud, and monitoring, and there is no single right method. The signal is matching statistical, distance, or model-based families to the dimensionality, distribution, and labels you actually have.Open full answer →
118How do you build anomaly detection for a streaming time series, and how do you handle seasonality and concept drift?▼hardNetflixUberMicrosoft2 replies◆ premiumThreshold alerts fire all weekend and miss the real outage Monday. The signal is decomposing seasonality first, choosing the right detector, and tuning for alert fatigue. Here is the answer that survives production.Open full answer →
11Design an anomaly detection system for a metric (e.g. cloud billing) with seasonality and cold start.▼hardAmazonMicrosoftGoogle1 replies○ sign inAnomaly detection sounds easy until seasonality, cold start, and alert fatigue hit. The signal is modeling the expected baseline (including weekly and daily cycles), choosing unsupervised methods for scarce labels, and tuning to avoid drowning users in false alarms. Here is the design.Open full answer →
