Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 2 discussion

SageMaker managed warm pools allow instances to stay in a ready state between consecutive training jobs, which minimizes infrastructure startup times. This feature is ideal for scenarios with frequent or consecutive training jobs, as it avoids the time-consuming process of provisioning infrastructure for each job.

A. No, Managed Spot Training is used to reduce the compute cost for training B. Yes, Warm Pool allow to retain and re-use provisioned infrastructure, also use a persistent cache to store data across training job 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 parallelize training by distributing data across multiple instances. This doesn't reduce infrastructure startu ptime

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.

https://aws.amazon.com/about-aws/whats-new/2022/09/reduce-ml-model-training-job-startup-time-8x-sagemaker-training-managed-warm-pools/

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.

https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html "which speeds up start times by reducing the time spent provisioning resources."