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Democratizing Machine Learning In Anomaly Detection


Anomaly detection company Prelert has released an Elasticsearch Connector to help developers deploy its machine learning-based Anomaly Detective engine on an Elasticsearch ELK (Elasticsearch, Logstash, Kibana) stack.

Elasticsearch is an open source, distributed, real-time search and analytics engine for use in distributed environments where there is a need beyond simple full-text search.

Earlier this year, Prelert released its Engine API for developers and to use its analytics algorithms in their operations monitoring and security architectures. By offering an Elasticsearch Connector, the company hopes to democratize the use of machine learning technology, providing tools to identify threats and opportunities hidden within massive datasets.

Written in Python, the Prelert Elasticsearch Connector source is available on GitHub. This enables developers to apply Prelert's machine learning based analytics to fit the big data needs within their own environment.

"Prelert's Anomaly Detective processes huge volumes of streaming data, automatically learns normal behavior patterns represented by the data and identifies and cross-correlates any anomalies. It routinely processes millions of data points in real-time and identifies performance, security, and operational anomalies so they can be acted on before they impact business," said Mark Jaffe, CEO, Prelert.

The Elasticsearch Connector is the first connector to be officially released by Prelert. Additional connectors to several of the most popular technologies used with big data will be released throughout the coming months.


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