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

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).

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Transcript

  1. Alejandro Guirao
    @lekum
    From 0 to anomaly detection in your
    infrastructure metrics in 15 minutes
    github.com/lekum

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  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”)

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  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)

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  4. ◉ Encoders
    ◉ Spatial Poolers -> Sparsely Dense Representation
    ◉ Temporal Pooler -> Cortical Learning Algorithm
    HTM concepts

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  5. Cortical Learning Algorithm

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  6. ◉ NuPIC
    ◉ C++/Python/Java/Clojure
    ◉ HTM Studio for Anomaly Detection
    ◉ Grok
    HTM implementations

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  7. ◉ https://github.com/numenta/nupic/wiki/Learning-NuPIC
    ◉ http://numenta.org/htm-school/ (Videos)
    ◉ https://github.com/lekum/htm-demo
    Further reading

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  8. Happy hacking!
    Alejandro Guirao
    @lekum
    lekum.org
    github.com/lekum

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