Machine learning and anomaly detection are being mentioned with increasing frequency in performance monitoring. But what are they and why is interest in them rising so quickly?
From Statistics to Machine Learning
There have been several attempts to explicitly differentiate between machine learning and statistics. It is not so easy to draw a line between them, though.
For instance, different experts have said:
- “There is no difference between Machine Learning and Statistics” (in terms of maths, books, teaching, and so on)
- “Machine Learning is completely different from Statistics.” (and the only future of both)
- “Statistics is the true and only one” (Machine Learning is a different name used for part of statistics by people who do not understand the real concepts of what they are doing)
In short we will not answer this question here. But for monitoring people it is still relevant that the machine learning and statistics communities currently focus on different directions and that it might be convenient to use methods from both fields. The statistics community focuses on inference (they want to infer the process by which data were generated) while the machine learning community puts emphasis on the prediction of what future data are expected to look like. Obviously the two interests are not independent. Knowledge about the generating model could be used for creating an even better predictor or anomaly detection algorithm.