IT Security professionals can protect from advanced threats with security analytics. Machine learning anomaly detection finds the fingerprints of criminal activity in real-time so that you can detect problems early and act fast.
IT Ops, APM and DevOps professionals no longer need to use antiquated technology. With machine learning anomaly detection you can automate data analysis and detect problems early so that you can act fast - before users detect an issue.
Our VP of Security Products, Mike Paquette, demonstrates use cases that show how to know normal and detect abnormal by using machine learning anomaly detection.
Rich Collier shares real use cases to demonstrate how businesses have transformed their IT Operations with machine learning anomaly detection.
Our QuickMode feature turns time-charts into real-time anomaly detection searches with a button click. Keep a constant eye on your data without having to sit there and eyeball those charts. We will do the work for you, and alert you when something changes or behaves abnormally.
At VentureBeat's 2014 big data conference, DataBeat, Prelert participated in the Innovation Showcase. At the event we discussed the importance of analytics, the significance of anomaly detection for end users, and the future of anomaly detection and machine learning.
Prelert's Product Manager provides an overview of our Anomaly Detective engine solving a difficult security case leveraging the Elasticsearch ELK stack.
Originally delivered at Data Science London, this talk looks at the problem of anomaly detection in large scale IT environments. Rule-based approaches are unmanageable and can't handle the complexity and scale of the data. Advanced Data Mining techniques are useful for improving the quality and scale of anomaly detection.
Prelert's Director of Research and Development discusses anomaly detection from a more technical perspective, points out some of the data characteristics which make anomaly detection for real world problems challenging and describes some of the techniques we use for anomaly detection.
This short (3:29) video is a playful look at how Anomaly Detective acts as your personal machine intelligence partner, learning the normal behavior of your data and monitoring it for abnormal behavior. No more writing rules and thresholds for complex (and often changing) data.
Prelert's Rich Collier shows how Anomaly Detective enables proactive, real-time monitoring without thresholds, and slashes complex problem troubleshooting times.
In this short video Prelert's CTO, Steve Dodson, explains the approach and analytics that make real-time anomaly detection possible on all Splunk data.
Responsys' Sr. Security Architect shows how he is detecting advanced threats and reducing thousands of IDS alerts to an actionable few with Prelert's Anomaly Detective.
SNAP Interactive deploys dozens of production changes a day making traditional monitoring based on thresholds pretty much useless. Their Principal Architect, Nick DiSanto, explains why the say "Prelert + Splunk = Winning Combination."
TRAC Research principal analyst Bojan Simic and Prelert CEO Mark Jaffe discuss the best practices that achieve proactive monitoring amidst huge volumes of APM data.
Director of Product Management, Rich Collier explains Prelert's mission, 'data science packaged for everyday decisions,' and how that manifests in our automated anomaly detection analytics product for Splunk.