Hype Cycle for Data Science and Machine Learning, 2017

Archived Published: 28 July 2017 ID: G00325005

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The hype around data science and machine learning has increased from already high levels in the past year. Data and analytics leaders should use this Hype Cycle to understand technologies generating excitement and inflated expectations, as well as significant movements in adoption and maturity.

Table of Contents

  • Analysis
    • What You Need to Know
    • The Hype Cycle
      • New Entrants
      • Name Changes
    • The Priority Matrix
    • Off the Hype Cycle
    • On the Rise
      • Human-in-the-Loop Crowdsourcing
      • Artificial General Intelligence
      • Conversational Analytics
      • Algorithm Marketplaces
      • Guided Analytics
      • Notebooks
      • AutoML
      • Embedded Analytics
      • IoT Edge Analytics
      • Advanced Anomaly Detection
      • Citizen Data Science
    • At the Peak
      • Augmented Data Discovery
      • Graph Analytics
      • Optimization
      • Prescriptive Analytics
      • Deep Learning
      • Machine Learning
      • Self-Service Data Preparation
      • Event Stream Processing
      • Cognitive Computing
      • Python
      • Data Lakes
      • Predictive Analytics
    • Sliding Into the Trough
      • Spark
      • Speech Analytics
      • Model Management
    • Climbing the Slope
      • Text Analytics
      • Video/Image Analytics
      • Ensemble Learning
      • Simulation
    • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels
  • Gartner Recommended Reading
© 2017 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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