Gartner Research

Hype Cycle for Data Science and Machine Learning, 2017

Published: 28 July 2017

ID: G00325005

Analyst(s): Peter Krensky , Jim Hare

Summary

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
  • 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

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