Gartner Research

Assessing the Open-Source, Enterprise Machine Learning Stack

Published: 20 June 2019

ID: G00386494

Analyst(s): Sumit Agarwal

Summary

Open source has been responsible for the growth of machine learning, and organizations are eager to take advantage and understand enterprise-scale viability. This research provides data and analytics technical professionals with an actionable assessment of the end-to-end, open-source ML stack.

Table Of Contents

Analysis

  • What Is Open Source?
    • Open-Source Licenses
    • Open-Source Selection Criteria
  • Open Stack ML Software Security
  • Open Stack Machine Learning
    • Programming Languages
    • Integrated Development Environment
    • Machine Learning and Deep Learning Packages
    • Model Serving
    • Container Orchestration
    • Container
    • Service Monitoring
    • Model Management System
  • Machine Learning Workload
  • Strengths
  • Weaknesses

Guidance

  • Buy Versus Build Conundrum
  • Evaluate the Strength of Each Layer in the ML Stack
  • Focus on One ML Step at a Time

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