Topic 1 Question 2
Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company is experimenting with consecutive training jobs. How can the company MINIMIZE infrastructure startup times for these jobs?
Use Managed Spot Training.
Use SageMaker managed warm pools.
Use SageMaker Training Compiler.
Use the SageMaker distributed data parallelism (SMDDP) library.
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- 正解だと思う選択肢: B
https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html#train-warm-pools-how-it-works SageMaker managed warm pools let you retain and reuse provisioned infrastructure after the completion of a training job to reduce latency for repetitive workloads, such as iterative experimentation or running many jobs consecutively.
👍 4andy_102024/12/01 - 正解だと思う選択肢: B
https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html "which speeds up start times by reducing the time spent provisioning resources."
👍 2GiorgioGss2024/11/27 - 正解だと思う選択肢: B
SageMaker managed warm pools are designed to reduce infrastructure startup times by keeping the training environment (instances, containers, and environment setup) ready between consecutive training jobs.
👍 2tigrex732024/11/27
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