Elastic, the company behind Elasticsearch, and the Elastic Stack, introduced 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.
"Our vision is to take the complexity out and make it simple for our users to deploy machine learning within the Elastic Stack for use cases like logging, security, and metrics," said Shay Banon, Elastic Founder and CEO. "I’m excited that our new unsupervised machine learning capabilities will give our users an out-of-the-box experience, at scale to find anomalies in their time series data, and in a way that is a natural extension of search and analytics."
Now available in the 5.4 release as a feature in X-Pack, the first set of Elastic’s unsupervised machine learning features automates anomaly detection in time series data, such as log files, application and performance metrics, network flows, or financial/transaction data. By utilizing existing and continuous data stored in Elasticsearch, Elastic’s new machine learning capabilities provide users with an out-of-box experience to operationalize their workstreams and use cases like logging, security analytics, and metrics analytics, in real-time, create sophisticated machine learning jobs using a familiar, user-friendly Kibana UI, and minimize complexity and painful integration.
Additional benefits include:
- Installs into Elasticsearch and Kibana with a single command as part of X-Pack
- Native integration with the Elastic Stack; no need to move data out of Elasticsearch
- An intuitive UI for creating machine learning jobs and analyzing anomaly detection results across diverse data types (log messages, network traffic, metrics)
- Runs within Elasticsearch - highly scalable and highly available
- Full support for X-Pack’s alerting features for proactive notifications
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