Gartner Research

A Roadmap for IT to Help Drive Machine Learning

Published: 20 June 2018

ID: G00356836

Analyst(s): Alexander Linden , Erick Brethenoux , Shubhangi Vashisth

Summary

This note looks at the lessons learned from hundreds of inquiries and a recent data science survey to outline some best practices for driving machine learning projects. Our findings are geared to guide data and analytics leaders, but should be applicable for other stakeholders too.

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • Create a Portfolio of AI and ML Ideas
  • Evaluate Ideas in Terms of ROI and Risks
  • Launch, Run and Deploy ML Projects
  • Further Improving the Data Science Capabilities of the Organization

Gartner Recommended Reading

©2019 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?

Become a client