How to use anomaly detection in Azure machine learning

Machine learning is about more than vision and speech, as Azure’s latest machine learning service shows

One key part of Microsoft’s big bet on machine learning is that these technologies need to be democratized, turned into relatively simple-to-understand building blocks that Microsoft’s developer audience can quickly learn and use in their own applications.

That’s where Azure’s Cognitive Services come in. Instead of having to understand the layers of training that go into the ResNet50 deep learning neural network, or how to build learning platforms using TensorFlow or Microsoft Cognitive Toolkit (CNTK), these services are just APIs that are ready to use. Microsoft has already trained the neural nets for these services, and it continues to tune them and use real-world operations as a foundation for future improvements. They’re cheap to use compared to the compute and storage cost of building and running your own machine learning algorithms.

The machine learning tools on Azure have rapidly become an important resource for anyone wanting to add basic artificial intelligence to an app. It’s important to know that they are limited, with a focus on three key areas: computer vision, text analysis, and speech recognition. They’re all important areas, but they are a limited subset of what can be done with modern machine learning.

Azure Cognitive Services enters a new AI area

Fortunately, the first new cognitive service to explore other aspects of machine learning entered beta recently: adding anomaly detection to the roster. Anomaly detection is an important AI tool, analyzing time-series data for items that are outside normal operating characteristics for the data source. That makes it an extremely flexible tool because modern businesses have a lot of streamed data, from financial transactions to software logs to device telemetry. The ability to use one API to work across all these different feeds shouldn’t be underestimated, because it makes building appropriate software a lot easier.

Normally anomaly detection takes time to set up. You need to train your model against a large amount of data to determine what’s normal operation and what’s out of the ordinary. It’s how credit-card fraud-detection systems build a model of your spending (and of all their customers’ habits) to detect when a compromised card is used and to block any future transactions to keep losses to a minimum.

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