Conversation until 7,
Then feature presentation —
Abstract and bio:
More companies, from startups to large enterprises, are storing large amounts of structured and unstructured data, especially in Elasticsearch. With ‘search’ becoming the foundation for many of these companies to address their most complex use cases, users need an an automated way to understand the ‘why’ in their data and take action on ‘difficult to see’ insights. Machine Learning is the next step to making this happen.
Prelert is a Behavioral Analytics platform that uses Machine Learning to analyze log data, find anomalies within the data, and links them together. We allow your data to tell its story.
Our machine learning-based behavioral analytics platform automatically detects abnormal behavior patterns in your data – identifying changes that can impact application performance/availability or identifying behaviors that may be indicative of advanced security threats.
Detecting advanced security threat activities and anomalies in log data. Discovering hidden fraud patterns in highly sensitive data. Identifying anomalous systems or metrics and their root causes across IT systems. Linking together complex series of events in data to expose early warning signals. Automatically pinpointing where and why critical system outages are occurring. Detecting unexpected drops in transactional activity, and much more.
In this talk, we will demonstrate how to combine and automate the use of search, and machine learning to get a comprehensive view of all of your data.
Morgan Goeller is a Solutions Architect for Elastic, focusing on real-time analytics and visualization. He has been in the tech industry for the last 20 years, working with customers in Digital Marketing, Telecommunications, Energy, and Healthcare. Morgan has a degree in Mathematics with an emphasis in Scientific Computation and lives in Austin, TX.
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