How should the company deploy the ML model to meet these requirements?
Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.
Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.
Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.
Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.
Explanations:
This option relies on AWS services and requires internet connectivity to upload images to S3 and invoke Lambda functions. Since the requirement specifies that feedback must be provided even if the internet is down, this solution does not meet the needs.
Deploying AWS IoT Greengrass on the local server allows the ML model to run locally, enabling real-time inference directly from the cameras. This setup ensures that local feedback can be provided regardless of internet connectivity, as the Greengrass component can directly call the local API when defects are detected.
Although using AWS Snowball can provide local compute capabilities, it still requires setup and management of the device, which may not be feasible for real-time inference. Additionally, the Snowball device might not be immediately available on-site, leading to delays. This option does not ensure continuous operation during internet outages.
Amazon Monitron is a specialized solution primarily for monitoring equipment health and may not be suited for general defect detection in manufacturing. It requires integration with the Monitron Gateway, which could introduce additional complexity and reliance on AWS services, thereby failing to meet the requirement for local operation during internet outages.
I guess:
Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.
I gauge that the answer is:
Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.