Gartner Information Technology Research

Critical Capabilities for Data Science and Machine Learning Platforms

Published: 02 March 2020

ID: G00391146

Analyst(s): Pieter den Hamer, Alexander Linden, Carlie Idoine, Erick Brethenoux, Jim Hare, Svetlana Sicular, Farhan Choudhary, Peter Krensky

Summary

The functions and features of data science and machine learning platforms are evolving quickly to keep pace with a highly innovative space. This research helps data and analytics leaders to evaluate 16 of these platforms across 15 critical capabilities.

Table Of Contents

What You Need to Know

Analysis

  • Critical Capabilities Use-Case Graphics
  • Vendors
    • Altair
    • Alteryx
    • Anaconda
    • Databricks
    • Dataiku
    • DataRobot
    • Domino
    • Google
    • H2O.ai
    • IBM
    • KNIME
    • MathWorks
    • Microsoft
    • RapidMiner
    • SAS
    • TIBCO Software
  • Context
  • Product/Service Class Definition
  • Critical Capabilities Definition
    • Data Access
    • Data Preparation
    • Data Exploration and Visualization
    • Augmentation (Automation)
    • User Interface
    • Machine Learning
    • Other Advanced Analytics
    • Flexibility and Openness
    • Performance and Scalability
    • Delivery
    • Platform and Project Management
    • Model Management
    • Precanned Solutions
    • Collaboration
    • Coherence
  • Use Cases
    • Business Exploration
    • Advanced Prototyping
    • Production Refinement
    • Augmented Data Science and Machine Learning
  • Vendors Added and Dropped
    • Added
    • Dropped

Inclusion Criteria

  • Exclusion Criteria
  • Critical Capabilities Rating

Appendix

  • Honorable Mentions

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