New MapR Quick Start Solution Accelerates Deep Learning Application Deployments

SOURCE: MapR Technologies

SAN JOSE, CA and LONDON — May 23, 2017 — MapR Technologies, provider of the Converged Data Platform that enables organizations to create intelligent applications that fully integrate analytics with operational processes in real time, today announced at Strata London a new Quick Start Solution (QSS) focusing on deep learning applications.  The MapR Distributed Deep Learning QSS is a data science-led product and services offering that enables the training of complex deep learning algorithms (i.e. deep neural networks, convolutional neural networks, recurrent neural networks) at scale.  Within a few weeks, the new QSS provides an environment for continuous learning, enables experimentation with deep learning libraries, and delivers a production framework for quickly operationalizing deep learning applications.

The MapR Distributed Deep Learning QSS leverages expertise from implementing advanced machine learning environments for MapR customers.  The new offering features access to distributed deep learning libraries (e.g. TensorFlow, Caffe, mxnet, etc.), a framework that intelligently switches storage and workflow between CPU and GPUs, and the stability, scale and performance of the Converged Data Platform to form the basis for advanced, data-driven applications.  Use cases for distributed deep learning technologies include:

– Extracting insights from images/video: Improve business outcomes from processing and analyzing images and video, such as ultrasounds, dashboard cameras, drone computer vision, satellite images, surveillance footage, etc.

– Understanding and predicting sequence of events:  Predicting behaviors or understanding patterns based on analysis of sequenced audio files, language models (natural language processing), written texts/social media posts, and analysis of time series data will allow businesses to stay one step ahead of expected outcomes. Businesses can use this knowledge to better predict how customers will react to a certain event, offer customers more relevant recommendations, streamline operations etc.

– Classification and forecasting: Increase accuracy and reliability of existing statistical and predictive models with more complex algorithms and advanced modeling. Applying  deep neural networks provides more reliable forecasts, and delivers better analysis from unsupervised learning

“Deep learning algorithms can provide profound transformational opportunities for an organization,” said Anil Gadre, chief product officer, MapR Technologies. “Our expertise in advance machine learning deployments coupled with the unique design of the MapR Platform form the foundation for our new offering.  The QSS will enable companies to quickly take advantage of modern GPU-based architectures and set them on the right path for scaling their deep learning efforts.”

To learn more about the new MapR Distributed Deep Learning QSS, visit here.

To download an image of the MapR Distributed Deep Learning QSS Reference Architecture, visit here.

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About MapR Technologies

Headquartered in San Jose, Calif., MapR provides the industry’s only Converged Data Platform that enables customers to harness the power of big data by combining analytics in real-time to operational applications to improve business outcomes. With MapR, enterprises have a data management platform for undertaking digital transformation initiatives to achieve competitive edge. Amazon, Cisco, Google, Microsoft, SAP, and other leading businesses are part of the global MapR partner ecosystem. For more information, visit www.mapr.com.