Elastic Adds Machine Learning into the Elastic Stack
(Thomson Reuters ONE) -
The first set of unsupervised machine learning capabilities simplifies anomaly
detection for time series use cases
MOUNTAIN VIEW, Calif. and AMSTERDAM, The Netherlands, May 04, 2017 (GLOBE
NEWSWIRE) -- 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.
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"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."
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.
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
Learn More
* Elastic Machine Learning Blog
* Get Started with Machine Learning in X-Pack
About Elastic
Elastic builds software to make data usable in real time and at scale for
search, logging, security, and analytics use cases. Founded in 2012, the company
develops the open source Elastic Stack (Elasticsearch, Kibana, Beats, and
Logstash), X-Pack (commercial features), and Elastic Cloud (a hosted offering).
To date, there have been more than 100 million cumulative downloads. Backed by
Benchmark Capital, Index Ventures, and NEA with more than $100 million in
funding, Elastic has a distributed workforce with more than 500 employees in 30
countries. Learn more at elastic.co.
Elastic Media Contacts:
AMER
Michael Lindenberger
Reidy Communications for Elastic
michael(at)reidycommunications.com
+1-415-531-1449
EMEA
Rory MacDonald
Age of Peers Ltd for Elastic
rory(at)ageofpeers.com
+44 (0)7899 965232
APAC
Janis Ma
Elastic Asia Pacific
janis(at)elastic.co
+852 3552 2927
This announcement is distributed by Nasdaq Corporate Solutions on behalf of Nasdaq Corporate Solutions clients.
The issuer of this announcement warrants that they are solely responsible for the content, accuracy and originality of the information contained therein.
Source: Elastic via GlobeNewswire
Unternehmensinformation / Kurzprofil:
Datum: 04.05.2017 - 20:00 Uhr
Sprache: Deutsch
News-ID 540503
Anzahl Zeichen: 5800
contact information:
Town:
Mountain View
Kategorie:
Business News
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