A manufacturing company is building an inspection solution for its factory.

The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information,react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports MLinference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.

The other options are not correct because:

Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory’s internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.

Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.

Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.