Exam AWS Certified Machine Learning - Specialty topic 1 question 301 discussion

https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html

CD Given the specific context of training a DeepAR forecasting model and the potential cost implications, the options B and D are generally more applicable and cost-effective approaches to decreasing training time. However, if cost is not a concern and the DeepAR algorithm can benefit significantly from GPU acceleration, then option C could be a valid approach as well.

C and D

CD is correct answer

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits: C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPUbased EC2 instances such as ml.p2 and ml.p33. D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3. The other options are not effective or relevant, because they have the following drawbacks: