If a hatch in a space station is even millimeters bigger or smaller than the required measurements, do you know what happens? Implosion, the sheer pressure the space exerts on the station, will not tolerate even a millimeter of error. So then, how do you expect a Machine Learning model or your customer base to be intolerant?
Anomaly Detection has become the need of the hour with the sheer amount of raw data available in the sphere. With small, skewed values in the data pre-training or frauds and misuse of your services, anomaly detection goes a long way to cut cost, time and boost performance.
Anomalies can be described as specific data points that are significantly different from the general trend of the data. Let us look at how this process can help your MLOps pipeline:
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.
However, the road to getting these performance boosts for your pipeline is straightforward, with new and upcoming techniques tested by organizations and teams worldwide. Let us look at some of such methods and techniques:
Isolation Forest: Isolation Forest processes randomly subsampled data in a tree structure using random characteristics. Deeper samples require more cuts. Thus, anomalies are less likely. Shorter branches disclose irregularities since the tree can quickly spot them.
Outlier Detection Factor: A data point's local density difference from its neighbors. Outliers are samples having a significantly lower density than their neighbors.
Mahalanobis Distance: Mahalanobis distance measures the distance between a point and a distribution. This method is appropriate for one-class classification and unbalanced data.
What you see here are just the techniques to identify anomalies for Unsupervised Learning; wanna learn more about other learning methods like Supervised and Semi-Supervised? Check out a detailed breakdown of anomaly detection and various methods to deal with it!
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