How to Operationalize Machine Learning and Data Science Projects

Published: 03 July 2018 ID: G00333499

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The democratization of machine learning platforms is proliferating analytical assets and models. The challenge now is to deploy and operationalize at scale. Data and analytics leaders must establish operational tactics and strategies to secure and systematically monetize data science efforts.

Table of Contents

  • Introduction
  • Analysis
    • Establish a Close, Ongoing Dialogue With Business Counterparts
    • Establish a Systematic Operationalization Process
      • Operationalization Cycle Functionality
      • Operationalization Cycle Process
      • Release Phase: Testing Models in Business Conditions
      • Activation Phase: Operating Models in Real Business Conditions
    • Monitor, Re-evaluate, Tune and Manage Models on an Ongoing Basis
    • Secure the Help of Nonanalytical Personnel
    • Monitor and Constantly Revalidate the Business Value Delivered by Machine Learning Models in Production
  • Gartner Recommended Reading
© 2018 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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