Which solution will meet these requirements?

By:
In: MLS-C01
Tagged:
With: 1 Comment
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 ื€ยขื€โ€™ 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?

Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.

Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.

Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Explanations:

While training on Amazon SageMaker and optimizing the model with SageMaker Neo is a good approach, this solution does not address the low-connectivity requirement at the facilities. SageMaker hosting services require continuous internet connectivity, which is not ideal for remote locations with limited connectivity.

Training the model on premises does not fully utilize the scalability of cloud resources. Additionally, uploading the model to an Amazon S3 bucket and deploying it via SageMaker hosting services does not solve the issue of low-connectivity environments.

This solution allows the model to be trained in the cloud using Amazon SageMaker with scalable compute resources. The model is then optimized with SageMaker Neo, and it is deployed on an edge device in the facility using AWS IoT Greengrass. This approach ensures the model can operate in low-connectivity environments since the edge device can run the model locally without needing continuous cloud connectivity.

While the model can be trained on-premises, this solution misses the use of cloud-based scalable resources for training. Deploying the model via AWS IoT Greengrass on the edge device addresses the low-connectivity issue but does not leverage the full potential of cloud training and optimization for cost and scalability.

2025-10-02

1 Comment

  1. Elijah
    Author

    I guess:
    Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Leave a Reply

Your email address will not be published. Required fields are marked *

12 − four =