Gartner Research

Preparing and Architecting for Machine Learning

Published: 17 January 2017

ID: G00317328

Analyst(s): Carlton Sapp

Summary

As machine learning gains traction in digital businesses, technical professionals must explore and embrace it as a tool for creating operational efficiencies. This primer discusses the benefits and pitfalls of machine learning, the requirements of its architecture, and how to get started.

Table Of Contents

Analysis

  • What Is Machine Learning?
  • What Business Trends and Benefits Are Driving Machine Learning?
    • Examples of How Machine Learning Can Deliver Value to Organizations
    • Machine Learning Can Also Provide Process Benefits for IT Organizations
    • Business Strengths and Challenges of Machine Learning
  • How Should IT Prepare for Machine Learning?
    • Learn the Stages of the Machine Learning Process
    • Understand the Model Development Life Cycle Needed for Machine Learning
    • Understand the Basic Architecture Needed for Machine Learning
    • A Comprehensive End-to-End Architecture
    • Understand What Skills Will Be Needed for Machine Learning
  • Steps to Get Started With Machine Learning
    • Learn About and Experiment With ML Concepts and Technology
    • Work Closely With Data Science Teams and Business Users to Identify a Use Case
    • Build a Use Case in the Cloud
    • Iteratively Expand Your ML Platform and Services Over Time

Conclusion

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