Machine Learning Engine Enables Non-Data Scientists

March 25, 2019
New Edge Analyzer is a portable, self-contained engine enabling predictive models to be deployed on edge devices.

Falkonry, Inc. launches the Edge Analyzer, a portable self-contained engine that enables customers to deploy predictive analysis on edge devices for low latency applications in disconnected environments or close to data sources. The new Edge Analyzer is available as part of Falkonry’s “pre-packaged” machine learning system, Falkonry LRS. Falkonry LRS is a predictive operations product that enables operations teams to create and deploy predictive analytics in the cloud or on-premises or on the edge - without requiring data scientists. 

“Many organizations can benefit from a packaged analytics application that includes embedded machine learning capabilities, especially when pursuing a common and well-defined advanced analytics problem, such as price optimization or fraud detection,” says Peter Krensky, sr. principal analyst and Carlie Idoine, sr. director analyst at Gartner in the March 2018 research, “How Data Science Teams Leverage Machine Learning and Other Advanced Analytics.”

“Before using Falkonry, companies often found themselves stuck in proof of concepts without a clear path for scaling to production,” says Dr. Nikunj Mehta, founder and CEO of Falkonry.  “The ability for operations teams to create and deploy predictive models at the edge and on premises has addressed the integration and skills gap challenges, while realizing five to ten times annual ROI.”

Falkonry LRS enables operations teams to discover, explain and predict behaviors that matter without requiring data scientists. The automated feature learning solves the most complex problem of applying machine learning on time series data saving time and building accurate predictive models. The explanation feature gives insight into model results, quantifying signal contribution and enabling SMEs to perform root cause analysis.

Edge Analyzers can be created in Falkonry LRS and transported for installation in remote or mobile environments. Minimal resource requirements allow for operation in constrained environments. They are configurable for high availability and can tolerate sensor and network outages. Use of containers enables runtime to be insulated from other processing activities. Each Edge Analyzer can be used to monitor multiple edge endpoints, and several Edge Analyzers can be deployed on a single computer to support multiple assessments.  Each Edge Analyzer includes a perpetual use license.

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