A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily. The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes. What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
A) Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
B) Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
C) Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
D) Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Question

Quizplus · Accepted Answer

Horovod is a distributed training framework that can be used to parallelize the training of TensorFlow models. By using Horovod, the Machine Learning Specialist can distribute the training across multiple machines, which will reduce the training time. Amazon SageMaker supports Horovod, so the Machine Learning Specialist can easily implement this solution without having to make any major changes to the code or infrastructure.

A Company's Machine Learning Specialist Needs to Improve the Training