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? (Choose two.)
A.
Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
B.
Use Amazon SageMaker Ground Truth for data labeling.
C.
Deploy models by using AWS Lambda functions.
D.
Use AWS Trainium instances for training.
E.
Use PyTorch or TensorFlow with the distributed training option.
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.
I will go with A and D... they seems the most logic ones here.
upvoted 1 times
...
Log in to ExamTopics
Sign in:
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one.
So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Saransundar
1 month, 2 weeks agoGiorgioGss
1 month, 3 weeks ago