How does SageMaker AI differ from Amazon Bedrock?

Bedrock is mainly for building generative AI applications on existing foundation models, while SageMaker AI is for developing and operating your own machine learning models with your own data.

Can I still use SageMaker AI features that are closed to new customers?

Clarify, Ground Truth, and Model Monitor are closed to new customers but remain available to existing customers; for new development, open-source or other AWS tools are alternatives.

What are the main inference options for deployed models?

SageMaker AI offers Real-Time, Serverless, and Asynchronous Inference plus Batch Transform, chosen by latency, traffic, and data volume needs.

Large Language ModelsQiita 機械学習9h agoNov 14, 2026, 16:00 JST

Amazon SageMaker AI: AWS's managed ML platform, renamed December 2024

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3 Key Points

What happened
Amazon SageMaker AI is AWS's managed service covering data prep, training, evaluation, deployment, and monitoring of custom machine learning models; it was renamed from Amazon SageMaker in December 2024.
Why it matters
For teams building demand forecasts, fraud detection, or other predictive models, the service is meant to spare them from setting up training servers, storage, and model-serving environments themselves.

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Context & Analysis

SageMaker AI is the custom-model counterpart to Bedrock, which handles generative AI on pre-built foundation models. That split matters: Bedrock users typically don't train models, while SageMaker AI users control training jobs, evaluation, and deployment environments. The December 2024 rename also broadens the SageMaker brand, since "Amazon SageMaker" now refers to a wider platform that includes data analytics and data management. Within that platform, a practical shift: several long-standing features are now closed to new customers or folded into other tools. Data Wrangler has been integrated into Canvas, and Autopilot's screen-based functions have moved to Canvas while its API remains available. For anyone following older tutorials built on Studio Classic or deprecated features, check current availability before starting a new system.

FAQ

How does SageMaker AI differ from Amazon Bedrock?: Bedrock is mainly for building generative AI applications on existing foundation models, while SageMaker AI is for developing and operating your own machine learning models with your own data.
Can I still use SageMaker AI features that are closed to new customers?: Clarify, Ground Truth, and Model Monitor are closed to new customers but remain available to existing customers; for new development, open-source or other AWS tools are alternatives.
What are the main inference options for deployed models?: SageMaker AI offers Real-Time, Serverless, and Asynchronous Inference plus Batch Transform, chosen by latency, traffic, and data volume needs.

Qiita 機械学習Read Original Article

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