Incorporating the Real Time Component into Analytics and Machine Learning

Incorporating the Real Time Component into Analytics and Machine Learning: Many industries and organizations today want to harness the power of big data analytics and machine learning for its potential to improve margins, enhance discoveries, give insight into the business, and enable fast data driven decisions. The challenges include inability and/or difficulties in using available systems, not knowing where to start or which tools make sense for a particular problem, and dealing with data sets that are too big, too fast, or too complicated to handle with traditional systems. RTDS Inc. has developed SymetryMLTM which are technologies for zero latency machine learning and analytics/exploration of very large datasets in real time, with a focus on speed, accuracy and simplicity. Our goal has been to cut the memory footprint required to learn large data sets, “reducer” functionality to automatically select the best attributes for model creation and build models on the fly. SymetryMLTM is also designed for easy integration into existing business processes via either an easy to use Web-UI or RESTful APIs. This talk will explore some of the functionality of these systems including real time exploration of data, fast multi-variate model prototyping, and our use of GPUs and parallelization. An example of brain related data and the complexities of analytics will be discussed as well as a brief overview of other verticals we are exploring. Our work is geared towards making big data make sense in real time and enable users to gain insights faster than traditional methods.

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