Q2Deployment and Orchestration of ML Workflows
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?
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B 4
Selected Answer: 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 a training job completes, helping reduce latency for repetitive workloads such as iterative experimentation or running many jobs consecutively.
B 4
Selected Answer: B
SageMaker managed warm pools let instances stay in a ready state between consecutive training jobs, which minimizes infrastructure startup times. This feature is a good fit for scenarios with frequent or consecutive training jobs, since it avoids the time-consuming process of provisioning infrastructure for each job.
B 2
Selected Answer: B
https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html
"which speeds up start times by cutting down the time spent provisioning resources."
B 2
Selected Answer: B
SageMaker managed warm pools are meant to reduce infrastructure startup times by keeping the training environment (instances, containers, and environment setup) ready between consecutive training jobs.
B 2
Selected Answer: B
https://aws.amazon.com/about-aws/whats-new/2022/09/reduce-ml-model-training-job-startup-time-8x-sagemaker-training-managed-warm-pools/
B 2
Selected Answer: B
A. No, Managed Spot Training is used to reduce the compute cost for training
B. Yes, Warm Pool allows you to retain and re-use provisioned infrastructure, and also use a persistent cache to store data across training jobs and help reduce infrastructure startup time as well as cost - https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html
C. No, SageMaker Training Compiler is used to optimize your code for a specific target architecture
D. No, the SageMaker distributed data parallelism (SMDDP) library is used to parallelize training by distributing data across multiple instances. This doesn't reduce infrastructure startu ptime
B 2
Selected Answer: B
SageMaker managed warm pools let instances remain in a ready state between consecutive training jobs, which minimizes infrastructure startup times. This feature is a good fit for scenarios with frequent or consecutive training jobs, since it avoids the time-consuming work of provisioning infrastructure for each job.
B 1
Sage maker managed warm pool support for easy start off with already existing infrastructure saving time and cost