Gartner Research

Machine Learning: FAQ From Clients

Published: 31 July 2017

ID: G00327948

Analyst(s): Jim Hare , Carlie Idoine , Alexander Linden , Svetlana Sicular , Rita Sallam , Nigel Shen, Peter Krensky , Shubhangi Vashisth

Summary

Gartner receives hundreds of different questions per week on machine learning, from all kinds of clients. This note offers data and analytics leaders concise answers and further drilldown pointers to the most frequently asked questions, and should help guide their planning process.

Table Of Contents

Analysis

  • 1. Definitions and Market Trends
    • How Do You Define Machine Learning?
    • How Does ML Work?
    • How Do You Define Artificial Intelligence?
    • Is the Current Hype Around AI and ML Justified?
    • What Are the Relationships Between ML, AI, Deep Learning, Data Science and Advanced Analytics?
    • What Business Problems Are Addressed by ML?
  • 2. Business Impacts
    • How Do We Evaluate the ROI From ML/AI Investments?
    • Do You Have Guidelines/Best Practices for ML/AI Contracts? How Do We Manage Any Issues Around Intellectual Property of Data?
    • How Do We Differentiate With ML?
  • 3. Market Size and Adoption
    • What Is the Market Size of ML?
    • What Are the Most Common ML Use Cases?
    • What Is Driving Further Adoption of ML?
  • 4. Technology
    • What Are the Technical Pitfalls of ML?
    • How Do We Implement ML Solutions?
    • What Is Augmented Analytics and How Does It Relate to ML?
  • 5. Organization
    • How Do We Get Started With ML?
    • How Do We Staff for ML?
    • What Are the Organizational Challenges of ML?
    • How Can We Be Sure the Vendor Claims of Deploying AI Is True?
  • 6. Infrastructure and Platform
    • How Are In-House-Developed ML Models Delivered Into the Organization?
    • Do We Need Special Infrastructure for ML?
    • Should We Use Cloud or On-Premises Infrastructure for ML?
  • 7. Other
    • What Newsletters Are There to Stay Up-to-Date?
    • What Are the Major Conferences and Summits?
    • How Can I Learn More About ML?

Gartner Recommended Reading

©2021 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. This publication may not be reproduced or distributed in any form without Gartner’s prior written permission. It consists of the opinions of Gartner’s research organization, which should not be construed as statements of fact. While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such. Your access and use of this publication are governed by Gartner’s Usage Policy. Gartner prides itself on its reputation for independence and objectivity. Its research is produced independently by its research organization without input or influence from any third party. For further information, see Guiding Principles on Independence and Objectivity.

Already have a Gartner Account?

Log In

Become a client

Learn how to access this content as a Gartner client.