Gartner Research

Hype Cycle for Data Science and Machine Learning, 2018

Published: 23 July 2018

ID: G00340329

Analyst(s): Peter Krensky , Jim Hare

Summary

The hype around data science and machine learning continues to defy gravity and soar to ever-higher levels. 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
  • The Priority Matrix
  • Off the Hype Cycle
  • On the Rise
    • Artificial General Intelligence
    • Conversational Chatbots for Analytics
    • Continuous Intelligence
    • Guided Analytics
    • Human-in-the-Loop Crowdsourcing
    • API Marketplaces
    • Embedded Analytics
    • IoT Edge Analytics
  • At the Peak
    • Machine Learning-Enabled Data Management
    • Advanced Anomaly Detection
    • Citizen Data Science
    • Optimization
    • Augmented Analytics
    • AutoML
    • Decision Management
    • Digital Ethics
    • Graph Analytics
    • Prescriptive Analytics
    • Deep Neural Nets (Deep Learning)
    • Machine Learning
    • Event Stream Processing
    • Cognitive Computing
    • Notebooks
  • Sliding Into the Trough
    • Data Preparation
    • Data Lakes
    • Predictive Analytics
    • Spark
    • Python
    • Speech Analytics
  • Climbing the Slope
    • Text Analytics
    • Model Management
    • Video/Image Analytics
  • Entering the Plateau
    • Ensemble Learning
  • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels

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