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Anomaly detection 101

What is anomaly detection? Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset’s normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior. Machine learning is progressively being used to automate anomaly detection.

Anodot Tutorial: Monitoring AWS Usage with Machine Learning

A 3-minute guide to help you start monitoring your AWS usage on Anodot's machine learning platform. Once it's up and running, Anodot will continuously monitor your AWS usage and deliver real-time alerts when there's an anomalous spike or drop. This powerful capability enables you to act quickly, far before costs get out of hand.

Anodot - Autonomous Business Monitoring

Business metrics are notoriously hard to monitor because of their unique context and volatile nature. Anodot’s Business Monitoring platform uses machine learning to constantly analyze and correlate every business parameter, providing real-time alerts and forecasts in their context. Anodot reduces detection and resolution for revenue-critical issues by as much as 70%. We have your back, so you’re free to play the offense and grow your business.

Anodot - Autonomous Business Monitoring

Business metrics are notoriously hard to monitor because of their unique context and volatile nature. Anodot’s Business Monitoring platform uses machine learning to constantly analyze and correlate every business parameter, providing real-time alerts and forecasts in their context. Anodot reduces detection and resolution for revenue-critical issues by as much as 80%. We have your back, so you’re free to play the offense and grow your business.