Gartner Research

Critical Capabilities for Data Science and Machine Learning Platforms


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.

Published: 02 March 2020

ID: G00391146

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

Table Of Contents


Strategic Planning Assumption(s)

What You Need to Know


  • Altair
  • Alteryx
  • Anaconda
  • Databricks
  • Dataiku
  • DataRobot
  • Domino
  • Google
  • IBM
  • MathWorks
  • Microsoft
  • RapidMiner
  • SAS
  • TIBCO Software
  • 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
  • Business Exploration
  • Advanced Prototyping
  • Production Refinement
  • Augmented Data Science and Machine Learning

Inclusion Criteria

Gartner Recommended Reading

Critical Capabilities Methodology


  • Vendors Added and Dropped
    • Added
    • Dropped

Inclusion Criteria

  • Exclusion Criteria
  • Critical Capabilities Rating


  • Honorable Mentions

Gartner Recommended Reading

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