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Streamlining AI/ML Development: AWS SageMaker HyperPod Examined

As artificial intelligence (AI) and machine learning (ML) gain market momentum, organizations are looking to build and manage their own models that balance cost, reliability, and specificity. A new report from Moor Insights & Strategy examines how AWS is addressing this need with its comprehensive AI technology stack and managed infrastructure offerings like Amazon SageMaker HyperPod.

Key insights from the report:

  • AWS offers a three-layer AI technology stack supporting a wide range of use cases and deployment options
  • Amazon SageMaker provides an end-to-end solution for the entire ML development lifecycle
  • SageMaker HyperPod delivers a fully managed AI/ML infrastructure that can reduce training time by up to 20% and lower costs
  • AWS's approach gives organizations more choice, faster time to market, and reduced complexity in building AI/ML models

The report recommends that organizations facing challenges in building and maintaining AI/ML infrastructure should consider Amazon SageMaker as a solid option for addressing these complexities.

Read the Moor Insights & Strategy report

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NOTICE

Moor Insights & Strategy, The Case for a Managed AI and ML Model Infrastructure, Jason Andersen, November 2024. This paper was commissioned by AWS. Moor Insights & Strategy disclaims all warranties as to the accuracy, completeness, or adequacy of information provided and shall have no liability for errors, omissions, or inadequacies in such information. This document consists of the opinions of Moor Insights & Strategy and should not be construed as statements of fact. The opinions expressed herein are subject to change without notice.