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Across industries, technologies, and use cases worldwide, there is perhaps no other data science strategy more important to understand and to leverage than anomaly detection.

As with most data science projects, the ultimate end goal or output of anomaly detection is not just an algorithm or working model. Instead, it’s about the value of the insight that outliers provide. That is, for a business, money saved from preventing equipment damage, money lost on fraudulent transactions, etc.. 

For a closer look at a variety of uses cases, get the latest guidebook by @Dataiku for an in-depth walk-through on executing on anomaly detection at scale.

Download here

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