Partner with research teams at The University of Melbourne and The University of Sydney, NextG team developed a cognitive ability evaluation system (Brain Efficacy System-BES) for the brain training program, www.activememory.com
BES runs advanced machine learning algorithms on Big data to produce real time higher quality results. If required, the system is capable to scale up to process millions of instructional sets (messages) required time frame (eg. In 1hr).
BES is a fully automated system and it is hosted by AWS. The Cloudformation tool is used for building and updating BES environments such as testing, staging and production environments.
Via Cloudformation, we create and update following resources and types,
Also continuous delivery pipeline is built using Atlassian Bamboo, EC2 server configuration management code written in Ansible and entire project handled and managed through Atlassian JIRA and JIRA agile software.
Technologies such as Loggly, Cloudwatch and NewRelic are used for logging, alerts, alarms and code analysis.
Music content summarization is a multi-disciplinary research topic which requires experts views from psychology, musicology and, machine learning. Researchers of the last decade have been trying to make a good business case around automatic music summarization so that interesting apps can be developed using the current technology.
In the music summarization project, backend system is developed using C++ and frontend system API and wrapper are developed using NodeJS.
Upload a song in the above link and listen to the machine generated music summary. Give us feedback to improve our music summarization engine.
NextGHub is a cloud based, mobile friendly, scalable, collaborative and dedicated e-learning platform developed by NextG team.