Making Machine Learning a Scalable Enterprise Reality — From Development to Production

Published: 29 November 2017 ID: G00343614


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Understanding the proper-machine learning apparatus plays a large role in shaping management and deployment of enterprisewide ML products. This assessment guides technical professionals through the optimal technologies and frameworks needed to start and maintain an enterprisewide ML initiative.

Table of Contents

  • Analysis
    • Introducing the Machine-Learning Apparatus
      • Operating Model and Governance
      • Resources
      • Business Operational Alignment
      • Data Science Tools for ML Design and Integrated Development
      • Unified Deep Learning Component
      • Adaptable Data Management
      • Deployment and Productionization/Operationalization (Publishing Platform)
      • User Experience
      • Infrastructure and Execution Environments
      • Tuning and Performance Engineering Models
    • Strengths
    • Weaknesses
  • Guidance
  • The Details
  • Gartner Recommended Reading
© 2017 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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