What are the purposes of anomaly detection?

Purposes of anomaly detection, as already mentioned in the above points, can be boiled down to the following topics:

Product Performance: An anomaly Detection paired with machine learning can correlate the existing data to cross-check while maintaining generalization and find odd standing products with complete knowledge of what makes them an anomaly.

Technical Performance: Any faults in your own deployed system may leave your server to active DDoS attacks. Such errors can also be proactively avoided and treated at the root using machine learning integrated into the DevOps pipeline.

Training Performance: During the pre-training phase, anomaly detection can come in handy, pointing out irregularities in the dataset, which may cause the model to over-fit and, in turn, act poorly.

There can be various types of anomalies the machine learning model can be trained to identify:

Point Anomaly: A tuple within the dataset that can be an anomaly if it is far from the trend set by the other data points.

Contextual Anomaly: Contextual anomalies can be considered an anomaly only if taken in a particular context and may even be valid if taken from another context.

Collective Anomaly: Such anomalies occur when data points in a whole collection of points act strange towards the other values, making the subset a complete rarity.

Now that we have the purpose and needs of anomaly detection, let us check out how anomaly detection algorithms can work with machine learning.