Gartner Research

Decision Point for Implementing Machine Learning to Deliver AI-Based Systems

Published: 19 February 2019

ID: G00377715

Analyst(s): Soyeb Barot

Summary

ML architectures are constrained by data gravity, compute requirements and model integration complexities. This Decision Point helps technical professionals select the right implementation strategy in order to operationalize the machine learning development life cycle to deliver AI-based systems.

Table Of Contents

Decision Point Question

Decision Overview

  • Architectural Context
  • Model Development Context
    • Build
    • Train
    • Infer

Decision Tool

Principles, Requirements and Constraints

  • Requirements and Constraints
    • Build Stage
    • Train Stage
    • Infer Stage

Alternatives

  • On-Premises
  • Cloud
  • Hybrid
  • IoT Edge
  • Products and Vendors

Future Developments

  • Optimization of ML Workloads in Multicloud Environments
  • Hybrid (On-Premises and Cloud) Environments Are the Next Frontier

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