Gartner Research

Critical Capabilities for Data Science and Machine Learning Platforms

Published: 04 April 2018

ID: G00335261

Analyst(s): Peter Krensky , Svetlana Sicular , Erick Brethenoux , Shubhangi Vashisth , Jim Hare , Carlie Idoine

Summary

The functions and features of data science and machine learning platforms are evolving quickly to keep pace with a highly innovative and hyped space. This research helps data and analytics leaders evaluate 17 data science and machine learning platforms across 15 critical capabilities.

Table Of Contents

What You Need to Know

Analysis

  • Critical Capabilities Use-Case Graphics
  • Vendors
    • Alteryx
    • Anaconda
    • Angoss
    • Databricks
    • Dataiku
    • Domino Data Lab
    • H2O.ai
    • IBM
    • KNIME
    • MathWorks
    • Microsoft
    • RapidMiner
    • SAP
    • SAS (Enterprise Miner)
    • SAS (Visual Analytics Suite)
    • Teradata
    • TIBCO Software
  • Context
  • Product/Service Class Definition
  • Critical Capabilities Definition
    • Data Access
    • Data Preparation
    • Data Exploration and Visualization
    • 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
  • Vendors Added and Dropped
    • Added
    • Dropped

Inclusion Criteria

  • Gate 1
  • Gate 2
  • Gate 3
  • Critical Capabilities Rating

Appendix

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