Gartner Research

China Summary Translation: 'How to Operationalize Machine Learning and Data Science Projects'

Published: 07 January 2019

ID: G00374217

Analyst(s): Melody Chien , Erick Brethenoux


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



How to Operationalize Machine Learning and Data Science Projects

  • Key Challenges
  • Recommendations



  • 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

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