AWS Certified Machine Learning – Specialty — Question 203

An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving.

Which solution should the company use to evaluate and improve the performance of the models?

Answer options

• A. Use Amazon CloudWatch algorithm metrics for visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
• B. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.
• C. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
• D. Use SageMaker Model Monitor for visibility into the ModelLatency metric and OverheadLatency metric of the model after the model is deployed. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.

Correct answer: C

Explanation

The correct answer is C because it leverages SageMaker Debugger to analyze the model's weights and apply pruning to enhance inference performance. Option A is incorrect as it suggests using Amazon CloudWatch instead of SageMaker Debugger. Option B does not include the pruning step, which is essential for optimizing the model. Option D focuses on monitoring after deployment rather than improving the model's performance during training.