Prelert, the anomaly detection company, has announced that it is enabling developers and power users to leverage its Anomaly Detective® advanced analytics engine through an open API. Enterprise users and developers, cloud service providers and technology vendors can now harness the power of machine learning based anomaly detection analytics to better manage complex online services, detect the earliest signs of advanced security threats and gain insight to business opportunities and risks represented by changing behaviors hidden in their massive data sets.
The massive data sets generated by the rapidly expanding world of the Internet of Things are so large and complex that it’s virtually impossible for humans to spot important behavioral changes that could solve key issues, reveal the next big ideas, or even open doors to whole new markets,” said Jeffrey M. Kaplan, Managing Director of IT strategic consulting firm, THINKstrategies, Inc. “The only way to overcome this challenge and capitalize on this unprecedented opportunity is to leverage machine-driven analytics that flag anomalous behaviors of significance so organizations can act on them.”
Prelert’s Anomaly Detective uses advanced analytics based on unsupervised machine learning to process huge volumes of streaming data, automatically learn normal behavior patterns represented by the data and identify and cross-correlate anomalies. Proven in more than 90 production installations, Prelert’s analytics engine routinely processes millions of data points in real-time and identifies performance, security and operational anomalies and their causes as they develop so they can be acted on before they impact business.
Explosive growth in computing scale and the accompanying data it generates have made it increasingly difficult for software developers and DevOps practitioners to discover problems that arise in these massive infrastructures,” said Donnie Berkholz, analyst at the developer-focused firm RedMonk. “By providing an open API to its anomaly detection engine, Prelert is addressing this need by enabling developers to leverage its machine learning tooling in their projects — whether the data is stored in a proprietary database, Hadoop, or another SQL or NoSQL data store.”
Prelert’s API provides a REST interface that allows real-time or batch analysis of massive data sets by its anomaly detection engine. A free license of Prelert’s anomaly detection engine and API is available for download at info.prelert.com for evaluation, development and testing. Pricing for production use is based on the number of concurrent analyses being run.
Prelert’s GitHub site contains open source tools and examples along with an open source UI based on Kibana.
Prelert has the only advanced machine learning anomaly detection engine that has been proven in more than 90 production installations,” said Mark Jaffe, Prelert’s CEO. “Through our work with Splunk and other partners, we’ve proven that the technology can provide an immediate return on investment. With the open API, developers can apply the technology wherever needed to make their software applications, IT environment or technology products successful.”
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