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Home / Tips and Tricks / Using AWS Lookout for statistics to detect anomalies in your data – CloudSavvy IT

Using AWS Lookout for statistics to detect anomalies in your data – CloudSavvy IT

AWS Lookout is a machine learning model that detects anomalies and unexpected changes in data. It can be used to send you alerts when your stats experience increased load or other extraordinary issues.

What is AWS Lookout for Statistics?

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7;t the first service of this name – AWS has Lookout for Vision, which searches for defects in products and automates quality inspection, and Lookout for Equipment, which monitors sensor data to detect anomalies.

Lookout for Metrics is arguably the most useful of all, as it can be tied to any kind of metric in your account and detect anomalies. CloudWatch already has some of these features with CloudWatch alarms, but using machine learning, Lookout for Metrics can detect more subtle issues.

Once a problem is detected, an impact summary of the anomaly is created, which can be sent to SNS or Lambda. You can then provide feedback and fine-tune the sensitivity of the alarms.

Lookout for Metrics can be connected to the following services, at least at the time of launch:

From there, alerts can be configured to be sent to AWS SNS and Lambda, which you can use to do whatever you want with it.

Lookout for Metrics simply costs $ 0.75 per metric, per month. If you have more than 1000 stats, the price will drop significantly.

Using Lookout For Metrics

Lookout is quite easy to use. All you need to do is create a detector, choose a data set and activate it.

Go to the Lookout Management Console and create a detector:

Give it a name and select the interval at which to run the detection. There are only a few options here, no support for cron syntax.

You can also change the default encryption key to select one from AWS KMS.

Next you need to add a dataset to this detector:

The exact configuration depends on the data source you are using. For example, S3 requires a path to the data object, as well as configuration for whether it is in CSV or JSON format.

After you have paired the source, you must enable the detector.

Once it starts making detections, you will be given the opportunity to review and rate them for their accuracy, which can improve it in the future.

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