A Guidance Framework for Operationalizing Machine Learning for AI


Published: 24 October 2018 ID: G00366587

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Summary

As AI and ML initiatives mature, the biggest challenge faced by technical professionals is operationalizing ML for effective management and delivery of models. This guidance document provides a framework to build and deploy ML models in production for successful delivery of AI-based systems.

Table of Contents

  • Problem Statement
  • The Gartner Approach
  • The Guidance Framework
    • Prework: Ideation and Team
      • Ideation to Set Platform Objectives
      • Technical Team
    • Step 1: Design the Target Architecture
      • Acquire
      • Organize and Analyze
      • Deliver Models for Inference
    • Step 2: Establish the Data Pipeline
      • Data Integration
      • Logical Data Warehouse (LDW)
      • Data Cleansing and Metadata
    • Step 3: Build the Machine Learning Architecture
      • Data Processing
      • Model Engineering
      • Model Execution
      • Model Deployment
    • Step 4: Implement a Model Management System
      • Model Registry
      • Model Manifestation
      • Model Servicing
      • Model Monitoring
      • Products and Tools
    • Step 5: Operationalize the Model-Building Process
      • Development Cycle
      • Test/Release Cycle
      • Activation Cycle
    • Follow-Up
  • Risks and Pitfalls
    • Spaghetti Bowl of ETL Scripts
    • Incoherent Technical Team
    • DIY Data Science
    • Inconsistent Provisioning of Models
    • Failing to Track and Monitor Models
      • Conclusion
  • 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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