Gartner Research

A Guidance Framework for Operationalizing Machine Learning for AI

Published: 24 October 2018

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.

Included in Full Research

  • 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
  • Spaghetti Bowl of ETL Scripts
  • Incoherent Technical Team
  • DIY Data Science
  • Inconsistent Provisioning of Models
  • Failing to Track and Monitor Models
    • Conclusion

Analysts:

Soyeb Barot

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