GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite, is launching GridGain Professional Edition 2.4, including a Continuous Learning Framework.
The addition of this framework in the latest update includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time.
“Companies can train models or run deep learning procedures directly on the data in the operational data set,” said Terry Erisman, VP of marketing at GridGain. “There’s a time and cost savings for that.”
GridGain Professional Edition 2.4 also enhances the performance of Apache Spark by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs.
GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.
The new GridGain Continuous Learning Framework is a building block for in-process HTAP (hybrid transactional/analytical processing) applications in which a data model is continually trained based on incoming data.
GridGain can now be used to store and manage Spark DataFrames. DataFrame support expands what was already the broadest support for Spark by any in-memory computing platform.
Companies with large data sets will benefit the most from these new machine learning capabilities, according to Erisman.
The company will continue to support and add new capabilities for Spark along with continually developing its SQL functionalities.
For more information about this news, visit www.gridgain.com.