Denis Magda on Continuous Deep Learning with Apache Ignite

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.