From 0 to Anomaly Detection in your infrastructure metrics in 15 minutes

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).

Transcript

1. Alejandro Guirao @lekum From 0 to anomaly detection in your infrastructure metrics in 15 minutes github.com/lekum
2. “ 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”)
3. ◉ 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)
4. ◉ Encoders ◉ Spatial Poolers -> Sparsely Dense Representation ◉ Temporal Pooler -> Cortical Learning Algorithm HTM concepts
5. Cortical Learning Algorithm
6. ◉ NuPIC ◉ C++/Python/Java/Clojure ◉ HTM Studio for Anomaly Detection ◉ Grok HTM implementations
7. ◉ https://github.com/numenta/nupic/wiki/Learning-NuPIC ◉ http://numenta.org/htm-school/ (Videos) ◉ https://github.com/lekum/htm-demo Further reading
8. Happy hacking! Alejandro Guirao @lekum lekum.org github.com/lekum