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Announcing: Distributed Deep Learning QSS

Blog Post created by adesai on May 23, 2017

We are pleased to announce the MapR Distributed Deep Learning QSSa 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, this new Quick Start Solution will provide an environment for continuous learning, enable experimentation with deep learning libraries, and deliver a production framework for quickly operationalizing deep learning applications.

 

The new offering features access to distributed deep learning libraries (TensorFlow, Caffe, MXNet, etc.), a framework that intelligently switches storage and workflow between CPUs and GPUs, and the stability, scale, and performance of the MapR Converged Data Platform to form the basis for advanced, data-driven applications such as the following:

 

  1. Convolutional Neural Networks for images
    Retail: in-store activity analysis of video to measure traffic
    Satellite images: labeling terrain, classifying objects
    Automotive: recognition of roadways and obstacles
    Healthcare: diagnostic opportunities from x-rays, scans, etc.
    Insurance: estimating claim severity based on photographs
  2. Recurrent Neural Networks for sequenced data
    Customer satisfaction: transcription of voice data to text for NLP analysis
    Social media: real-time translation of social and product forum posts
    Photo captioning: search archives of images for new insights
    Finance: Predicting behavior based via time series analysis (also enhanced recommendation systems
  3. Deep Neural Networks for Improved Traditional Algorithms
    Finance: Enhanced Fraud Detection through identification of more complex patterns
    Manufacturing: Enhanced identification of defects based on deeper anomaly detection

 

KEY SOLUTION CAPABILITIES

The Deep Learning Quick Start Solution is a major step towards transforming your business using deep learning. By the end of the engagement the customer can expect the following:

  • A MapR Converged Data Platform cluster installed and configured for efficient experimentation with deep learning libraries (such as TensorFlow on Kubernetes) and access to both CPUs and GPUs.
    An in-depth collaboration between business stakeholders and a deep learning scientist to identify the tools and methods that will provide the optimal results to the business problem.
  • A complete model-building initiative including experimentation with neural network parameters (number of nodes, learning rates, modeling layers, etc.) to achieve maximum performance gains.
    Training on implementation of model, interpreting reason codes, and applying model metrics to business goals. Stakeholders are trained on the process as a whole to ensure a clear path forward.
  • A fully-functional deep learning platform that will continue to fuel cutting-edge research and provide scalable access to the newest, most powerful algorithms as they become available.

 

Reference Architecture for Distributed Deep Learning on MapR

                  

Using this approach, MapR data scientists use the MapR Converged Data Platform and distributed machine learning algorithms to provide an enterprise-grade analytics capability that will continue to be refined and modified to respond to new data sets, new algorithms, and new intelligent applications.

 

KEY BUSINESS BENEFITS INCLUDE:

  • A scalable Deep Learning Platform that will continue to enable cutting-edge research opportunities long after the QSS has delivered initial results.
  • An enterprise class data platform with virtually limitless scale that supports a rich choice of open source and commercial processing engines and analytical tools
  • Extensive collaboration with key stakeholders to build a high quality, customized image classification model on which to build intelligent applications
  • Clear demonstration of value to business and technical stakeholders
  • Continuous training and knowledge transfer during the engagement about tools, techniques and use case roadmap

 

 

LEARN MORE

Visit the MapR Distributed Deep Learning QSS page.

Read Distributed Deep Learning on the MapR Converged Data Platform 

TensorFlow on MapR Tutorial

machine learning 

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