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Realize Superior Business Outcomes, Developer Efficiency, and Accelerated Innovation
with High-Performance, Cost-Efficient, and Easy-to-Use ML Infrastructure
IDC White Paper, sponsored by Amazon Web Services
October 2021 | Doc. #US48194621
Tame the Heterogeneity Through a Unified
Life-Cycle Management Platform
IDC recommends embracing the reality of a heterogeneous mix of AI use cases and
machine learning/deep learning models and taming such heterogeneity through
unified life-cycle management.
A unified life-cycle management service such as Amazon SageMaker enables
end users to build, train, test, and deploy machine learning models at scale.
Amazon SageMaker provides features to enable all phases of an ML pipeline end
to end, thereby accelerating innovations through AI enablement. Amazon SageMaker
enables end users to mix and match ML training algorithms, thereby providing them
with more control and flexibility. Using Amazon SageMaker also relieves end users of
the overhead of resource provisioning, thereby making MLOps easier.
IDC recommends taming the heterogeneity of AI/ML use cases through a unified
life-cycle management platform such as Amazon SageMaker.
