Procera Networks

Aspire Systems is an Outsourced Product Development firm committed to helping our customers build software products better and faster. We work with some of the world's most innovative Independent Software Vendors and software-enabled businesses, ranging from start-ups to established industry leaders, transforming the way software is built.Our approach to product development is based on the pioneering concept of Producteering that we have evolved and refined through years of product-building experience. Producteering is a set of Principles and Practices, driven by the right People and supported by the right Platform. Producteering, when applied rigorously, will always result in well-engineered, high-quality software.

Cloudera delivers an Enterprise Data Cloud for any data, anywhere, from the Edge to AI.Cloudera was founded in 2008 by some of the brightest minds at Silicon Valley’s leading companies, including Google (Christophe Bisciglia), Yahoo! (Amr Awadallah), Oracle (Mike Olson), and Facebook (Jeff Hammerbacher). Doug Cutting, co-creator of Hadoop, joined the company in 2009 as Chief Architect and remains in that role. Today, Cloudera has more than 1,600 employees. They have offices in 24 countries around the globe, with their headquarters in Palo Alto, California.

This initiative is developing new IoT (Internet of Things) technology that will significantly enhance the reliability and effectiveness of measuring human emotional responses to their surrounding physical environment.Sensing Feeling develops advanced IoT sensor technology powered by computer vision and deep-learning/AI to passively determine customer emotional response to the environment provided to them by our clients, helping them understand how engaged and delighted they are with the products and services provided to them, with potentially disruptive applications within health, education, in-store retail, corporate workspaces, and travel & leisure sectors.

DeepCube focuses on research and development of deep learning technologies that result in improved real-world deployment of AI systems.

By training AI models at the edge where the data is generated, one can train on the entire data generated, without compressing, or sampling the data in order to make it easily transferable to the cloud/server, and without the need to anonymise it for privacy reasons. This ability to decentralise the training process, and by doing so, allowing to train on entire datasets, will bring forth a potential new level of accuracy to the world of AI. These new obtainable accuracies can generate both high financial rewards in some verticals (retail, intelligent homes, manufacturing), and life-saving solutions in others (autonomous cars, medical).