Gartner Research

Integrating Machine Learning Into Your Application Architecture

Published: 08 February 2018

ID: G00320952

Analyst(s): Gary Olliffe


Machine learning is at the peak of hype, propelled by its association with artificial intelligence. Technical professionals responsible for application architecture must cut through the hype to understand how ML technology solves real-world problems and how to integrate it into their solutions.

Table Of Contents


  • What Is Machine Learning?
  • Why Is Machine Learning Relevant Now?
  • Developers: The Next Target for ML Technology Providers
  • ML's Impact on Modern Applications Is Localized and Integral
  • Options for Sourcing ML Capabilities
    • Using ML-Based APIs
    • Using Pretrained ML Model Definitions With an ML Framework
    • Training New Models
  • Strengths
  • Weaknesses


  • Application Architects Should Treat Machine Learning as a New Service Implementation Architecture
  • Learn the Basic Vocabulary and Skills to Understand ML
  • Use ML-Based APIs as a Starting Point
  • Build Working Relationships With Data Science Colleagues

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