Topic 1 Question 39
2 つ選択A company wants to improve the sustainability of its ML operations. Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs?
Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
Use Amazon SageMaker Ground Truth for data labeling.
Deploy models by using AWS Lambda functions.
Use AWS Trainium instances for training.
Use PyTorch or TensorFlow with the distributed training option.
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- 正解だと思う選択肢: AD
I will go with A and D... they seems the most logic ones here.
👍 1GiorgioGss2024/11/27 - 正解だと思う選択肢: AD
Blog: https://aws.amazon.com/blogs/machine-learning/optimizing-mlops-for-sustainability/ Sustainability Goals: instances are up to 25% more energy efficient than comparable accelerated computing EC2 instances; https://aws.amazon.com/ai/machine-learning/trainium/
SageMaker debugger helps to optimize resource consumption by detecting under-utilization of system resources, identifying training problems, and using built-in rules to monitor and stop training jobs as soon as bugs are detected.
👍 1Saransundar2024/12/04
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