Gartner Research

A Guidance Framework for Operationalizing Machine Learning for AI


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.

Published: 24 October 2018

ID: G00366587

Analyst(s): Soyeb Barot

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

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