by Angela Guess

A new press release reports, “Elastic, the company behind Elasticsearch, and the Elastic Stack, the most widely used collection of open source products for solving mission-critical use cases like search, logging, and analytics, announces the introduction of their first machine learning capabilities in Elastic’s 5.4 release. Based on the recent acquisition of Prelert, the new capabilities address the growing desire for customers to utilize machine learning technology, without the need for specialist in-house knowledge and custom development. Elastic’s new machine learning features provide a ready-built solution for any time series dataset, which automatically identifies anomalies, streamlines root cause analysis, and reduces false positives within real-time applications. The technology delivers rapid business benefits for companies trying to spot infrastructure problems, cyber attacks, or business issues in real-time.”

The release goes on, “As organizations seek to derive and operationalize real-time insights, the Elastic Stack has become one of the most widely used tools for developers and IT operations teams to use for collecting, enriching, and analyzing log files, security data, metrics, text documents, and more. However, as the data generated by such organizations increase in size and complexity, traditional approaches to data analysis become impractical. While third-party and off-the-shelf machine learning toolkits may offer capabilities to create statistical models, the biggest challenge lies in developing real-time operational systems for existing workstreams and use cases. Scarce and expensive data science skills are needed to figure-out the correct statistical models for different, diverse data sets, and hand-crafted rules are brittle and often generate many false-positives.”

Read more at Globe Newswire.

Photo credit: Elastic