Why it matters

SageMaker is AWS ML. Understanding shapes AWS ML ops.

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The architecture

Studio: notebook + tools.

Training: managed jobs.

Endpoints: real-time inference.

SageMaker platformStudionotebook + IDETraining + tuningmanaged jobsEndpointsinferenceSageMaker Pipelines for MLOps orchestration
SageMaker components.
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How it works end to end

Training: distributed + Spot.

Endpoints: real-time or async.

Model registry: versioning.

Pipelines: MLOps automation.

Ground Truth: labeling.