Amazon SageMaker Training Managed Warm Pools gives you the flexibility to opt in to reuse and hold on to the underlying infrastructure for a user-defined period of time. This is done while also maintaining the benefit of passing the undifferentiated heavy lifting of managing compute instances in to Amazon SageMaker Model Training. In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices.
Overview of SageMaker Training Managed Warm Pools
SageMaker Model Training is a fully managed capability that spins up instances for every job, trains a model, runs and then spins down instances after the job. You’re only billed for the duration of the job down to the second. This fully managed capability gives you the freedom to focus on your machine learning (ML) algorithm and not worry about undifferentiated heavy lifting like infrastructure management

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