Using the NuPIC framework (https://github.com/numenta/nupic/tree...), we will show the basics of Anomaly Detection using HTM ( Hierarchical Temporal Memory), and perform a demo trying to detect an anomaly in an infrastructure metric (such as a host CPU).
From 0 to anomaly detection in your
infrastructure metrics in 15 minutes
The checks are often inflexible Boolean logic or arbitrary static in time thresholds. They generally
rely on a specific result or range being matched.
The checks again don’t consider the dynamism of most complex systems.
A match or a breach in a threshold may be important or could have been triggered by an
exceptional event—or it could even be a natural consequence of growth.
James Turnbull (“The Art of Monitoring”)
◉ Biologically constrained theory of machine intelligence
◉ “On Intelligence” (2004, Jeff Hawkins)
◉ Numenta (2005)
Time-based learning algorithms that store and
recall temporal patterns
Hierarchical Temporal Model (HTM)
◉ Spatial Poolers -> Sparsely Dense Representation
◉ Temporal Pooler -> Cortical Learning Algorithm
Cortical Learning Algorithm
◉ HTM Studio for Anomaly Detection
◉ http://numenta.org/htm-school/ (Videos)