About this Learning Series

With this AI-driven DevOps aka AIOps, whenever anything abnormal happens in enterprise operation, it’s brought to the attention of anyone concerned along with contextual data, to help resolve the issue. What is abnormal is defined with reference to what is good or normal. What is normal is learnt by the system using Machine Learning. In other words, the system is Machine Learning driven and not rule driven. Various operational data streams are analyzed for building ML models and for the prediction of anomalies.

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What is covered

  • Exploratory data analysis using python to discover the category of data
  • Independent univariate  data anomaly detection on Spark/Scala
  • Autoregressive univariate  data anomaly detection on Spark/Scala
  • Multivariate  data anomaly detection on Spark/Scala
  • Multivariate  data anomaly detection on Spark/Scala
  • Data long term drift or change point detection on Spark/Scala
  • Anomaly aggregation based on data stream hierarchy on Spark/Scala
  • Anomaly thresholding  to control event flooding with Machine Learning on Spark/Scala

Target Audience

  • COO
  • CIO
  • DevOps
  • Software Engineers

What to Bring

  • Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well;  Personal laptops are recommended)
  • [nice to have] download our docker image elephantscale/es-training

Session Details

Session 1: Exploratory data analysis using python to discover the category of data(2020-10-02)

Session 2: Independent Univariate data anomaly detection on Spark/Scala(2020-10-16)

Session Recordings(On-demand)