Gartner Research

Preparing and Architecting for Machine Learning: 2018 Update

Published: 14 September 2018

Summary

Machine learning continues to gain traction in digital businesses, and technical professionals must embrace it as a tool for creating operational efficiencies. This updated primer discusses the benefits and pitfalls of machine learning, architecture updates, and new roles and responsibilities.

Included in Full Research

  • 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
    • Driving Business Value by Integrating Machine Learning and BPM
  • 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
    • Develop a Framework for Designing ML Workflows
    • Applying Machine Learning to Machine Learning
    • Leverage Machine Learning as a Service (MLaaS) to Accelerate Delivery
    • A Comprehensive End-to-End Architecture
    • Understand What Skills Will Be Needed for Machine Learning
  • Steps to Get Started With Machine Learning
    • Understand How Business Needs Intensify Based on Maturity of ML Environment
    • 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

Analysts:

Carlton Sapp

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