MLS-C01 · Question #205
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company…
The correct answer is C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using. Using S3 for scalable training and SageMaker Neo for compiling model for edge devices. https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html
Question
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ТВ of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center. The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company’s use of an ML model in the low-connectivity environments. Which solution will meet these requirements?
Options
- AMove the training data to an Amazon S3 bucket. Train and evaluate the model by using
- BTrain and evaluate the model on premises. Upload the model to an Amazon S3 bucket.
- CMove the training data to an Amazon S3 bucket. Train and evaluate the model by using
- DTrain the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge
How the community answered
(27 responses)- A11% (3)
- B26% (7)
- C56% (15)
- D7% (2)
Explanation
Using S3 for scalable training and SageMaker Neo for compiling model for edge devices. https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html
Topics
Community Discussion
No community discussion yet for this question.