Anomaly Detection
Detect anomalies on very large structured data sets
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Accelerator Description
Anomaly Detection is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. Such “anomalous” behavior typically translates to some kind of a problem like a:
- Credit card fraud,
- Failing machine in a server,
- A cyber-attack,
- Variation in financial transactions,
- And so on.
Common Anomaly Detection techniques are difficult to implement on very large sets of Data. The Anomaly Detection Accelerator, leverages the iJungle technique from Dr Ricardo Castro, which solves this challenge, enabling anomaly detection on large sets of data.
Prerequisites
- ​​​​​​​Azure Machine Learning (Ubuntu Linux compute)
Accelerator Components
