SageMaker HyperPod is a capability of SageMaker AI that provides an always-on machine learning environment on resilient clusters. You can use these clusters to run any machine learning workloads for developing state-of-the-art machine learning models such as large language models (LLMs) and diffusion models.

Describes the step types in Amazon SageMaker Pipelines.

Describes the machine learning environments supported by Amazon SageMaker AI.

Learn about Amazon SageMaker Canvas, a service that you can use to get machine learning predictions and build models without using any code.

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The then() method of Promise instances takes up to two arguments: callback functions for the fulfilled and rejected cases of the Promise. It stores the callbacks within the promise it is called on and immediately returns another Promise object, allowing you to chain calls to other promise methods.

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