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Datadog Announces Machine-Learning Based Anomaly Detection For Cloud Applications

By   /  October 31, 2016  /  No Comments

ddby Angela Guess

According to a recent press release, “Datadog, the essential monitoring service for modern cloud environments, today announced the release of a new machine-learning based feature called Anomaly Detection. This will allow engineering teams to quickly identify abnormal behavior within rapidly changing cloud environments, based on historical patterns that are impossible to track manually. ‘We are analyzing nearly a trillion data points every day coming from some of the largest companies in the world,’ said Homin Lee, Lead Data Scientist at Datadog. ‘Our algorithms are rooted in classic statistical models but have been heavily adapted and optimized by Datadog for monitoring cloud applications’.”

The release continues, “Anomaly Detection works by constantly analyzing historical application performance data, in order to evaluate whether the current state is to be expected by comparison. This is different from traditional monitoring solutions that pre-define what should be considered normal behavior of the application, without taking into consideration seasonality and trends. Application throughput, web requests, user logins and other top-level metrics all have pronounced peaks and valleys, and those fluctuations make it difficult to manually set sensible thresholds for alerting or investigation. ‘It can be challenging to manually configure alerts for metrics which change throughout the day, week, or year,’ said Igor Serebryany, Developer Happiness Engineer at Airbnb. ‘Anomaly detection helps us respond to issues more quickly, while avoiding needlessly paging our engineers’.”

Read more at Business Wire.

Photo credit: Datadog

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