Distributed Deep Learning

Many industries are undergoing groundbreaking transformation inspired by advancements in deep learning technology. The availability of unprecedented amounts of data and the leaps made in computational power are fundamental reasons why deep learning has become a viable technology, primed to be the disruptive force. A key example of this disruptive force is the transformation in the automotive industry. Deep learning is enabling the technology for autonomous driving vehicles, which is becoming a reality that will shape the future of the industry.

The Distributed Deep Learning Quick Start Solution from MapR 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. It features access to distributed deep learning libraries (e.g. TensorFlow, etc.), a framework that adroitly switches storage and workflow between CPU and GPUs, plus 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

    1. Retail: in-store activity analysis of video to measure traffic
    2. Satellite images: labeling terrain, classifying objects
    3. Automotive: recognition of roadways and obstacles
    4. Healthcare: diagnostic opportunities from x-rays, scans, etc.
    5. Insurance: estimating claim severity based on photographs
  2. Recurrent Neural Networks for sequenced data

    1. Customer satisfaction: transcription of voice data to text for NLP analysis
    2. Social media: real-time translation of social and product forum posts
    3. Photo captioning: search archives of images for new insights
    4. Finance: Predicting behavior based via time series analysis (also enhanced recommendation systems
  3. Deep Neural Networks for Improved Traditional Algorithms

    1. Finance: Enhanced Fraud Detection through identification of more complex patterns
    2. Manufacturing: Enhanced identification of defects based on deeper anomaly detection

What's Included?



Trial subscription of MapR Converged Data Platform Enterprise Premier for the duration of the quick-start.

professional services

Professional Services

3-10 weeks of engagement with MapR Professional Services Engineers and Data Scientists (Duration varies based upon the particular quick start.)



2 Academy Pro Subscriptions including Certification Exams.

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.

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.

Reference Architecture for Distributed Deep Learning on MapR

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


Deploy Distributed Deep Learning QSS on MapR GPU Cluster, Part 1

Deploy Distributed Deep Learning QSS on MapR GPU Cluster, Part 2

Distributed Deep Learning on MapR Converged Data Platform

Deep learning: What are my options

Scalable machine learning on the MapR Converged Data Platform via SparkR and H20