Gartner Research

Hype Cycle for Data Science and Machine Learning, 2019

Summary

Data and analytics leaders should use this Hype Cycle to understand key trends and innovations, including those related to improving expert and citizen data scientist productivity, implementing new algorithms for cutting-edge use cases, and scaling and operationalizing data science projects.

Published: 06 August 2019

ID: G00369766

Analyst(s): Shubhangi Vashisth Alexander Linden Jim Hare Peter Krensky

Table Of Contents

Analysis

  • What You Need to Know
  • The Hype Cycle
  • The Priority Matrix
  • Off the Hype Cycle
  • On the Rise
    • Quantum ML
    • Federated Machine Learning
    • Generative Adversarial Networks
    • Adaptive ML
    • Reinforcement Learning
    • Transfer Learning
    • AI Cloud services
    • Data Labeling and Annotation Services
    • Synthetic Data
    • Explainable AI
  • At the Peak
    • Continuous Intelligence
    • Edge Analytics
    • MLOps
    • Citizen Data Science
    • Decision Management
    • Augmented Analytics
    • AutoML
    • Conversational User Interfaces
    • Digital Ethics
    • Deep Neural Networks (Deep Learning)
    • Graph Analytics
    • Prescriptive Analytics
    • Advanced Anomaly Detection
    • Event Stream Processing
  • Sliding Into the Trough
    • Advanced Video/Image Analytics
    • Augmented Data Management
    • Python
    • Spark
  • Climbing the Slope
    • Predictive Analytics
    • Notebooks
    • Text Analytics
    • Traditional Model Management
  • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels

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