At the recent ApacheCon North America, Denis Magda spoke on continuous machine learning with Apache Ignite, an in-memory data grid. Ignite simplifies the machine-learning pipeline by performing training and hosting models in the same cluster that stores the data, and can perform "online" training to incrementally improve models when new data is available. Magda, vice-president of product management at GridGain, began by describing some of the pain points of machine learning on large datasets, in particular the latency involved in moving data across the network from its storage location to the processors that perform training. Models also have to be deployed into a production system after they are trained, and retrained periodically after new data is collected. Because Ignite runs code on the same computers that host data, it can train, deploy, and update a machine-learning model without a time-consuming extract-transform-load (ETL) step.

GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite(TM), today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which 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 now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark(TM) by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework 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.

FOSTER CITY, Calif., March 27, 2018 -- GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite, today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which 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 now build and continuously update models at in-memory speeds and with massive horizontal scalability. 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 Continuous Learning Framework 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.

Apache Spark (click here) is a general engine for large-scale analytical data processing which includes a powerful Machine Learning Engine (MLE). The GridGain in-memory computing platform (click here), built on Apache Ignite (to visit: click here), includes a comprehensive set of computing solutions including a data grid, compute grid, SQL grid, streaming, and acceleration solutions for Hadoop and Spark. GridGain and Spark are both in-memory computing solutions but they target different use cases. In many cases, they can be used together to achieve superior machine learning performance and functionality. GridGain can distribute and cache data in RAM across multiple servers to deliver unprecedented processing speed and massive application scalability.