AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with intermittent connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud.

ML Inference is a feature of AWS Greengrass that makes it easy to perform machine learning inference locally on Greengrass Core devices using models that are built and trained in the cloud. 
AWS Greengrass seamlessly extends AWS to devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With Greengrass, you can use familiar languages and programming models to create and test your device software in the cloud, and then deploy it to your devices. AWS Greengrass can be programmed to filter device data and only transmit necessary information back to the cloud. AWS Greengrass authenticates and encrypts device data at all points of connection using the security and access management capabilities of AWS IoT Core. This way, data is never exchanged between devices when they communicate with each other and the cloud, without proven identity.

Benefits :
Respond to Local Events in Near Real-time
AWS Greengrass devices can act locally on the data they generate so they can respond quickly to local events, while still using the cloud for management, analytics, and durable storage. The local resource access feature allows Lambda functions deployed on Greengrass Core devices to use local device resources like cameras, serial ports, or GPUs so that device applications can quickly access and process local data.

Operate Offline
AWS Greengrass lets connected devices operate even with intermittent connectivity to the cloud. Once the device reconnects, Greengrass synchronizes the data on the device with AWS IoT Core, providing seamless functionality regardless of connectivity.

Secure Communication
AWS Greengrass authenticates and encrypts device data for both local and cloud communications, so that data is never exchanged between devices and the cloud without proven identity. Greengrass uses the same security and access management you are familiar with in AWS IoT Core, with mutual device authentication and authorization, and secure connectivity to the cloud

Simplified Device Programming with AWS Lambda
AWS Greengrass uses the same AWS Lambda programming model you use in the cloud, so you can develop code in the cloud and then deploy it seamlessly to your devices. Greengrass lets you execute Lambda functions locally, reducing the complexity of developing embedded software

Reduce the Cost of Running IoT Applications
With AWS Greengrass you can program the device to filter device data locally and only transmit the data you need for your applications to cloud. This reduces the amount of raw data transmitted to the cloud and lowers cost, and increases the quality of the data you send to the cloud so you can achieve rich insight at a lower cost.

Use Cases
AWS Greengrass ML Inference can be deployed on connected devices like security cameras, traffic cameras, body cameras, and medical imaging equipment to help them make predictions locally. With AWS Greengrass ML Inference, you can deploy and run ML models like facial recognition, object detection, and image density directly on the device. For example, a traffic camera could count bicycles, vehicles, and pedestrians passing through an intersection and detect when traffic signals need to be adjusted in order to optimize traffic flows and keep people safe.
Retail and Hospitality
Retailers, cruise lines, and amusement parks are investing in IoT applications to provide better customer service. For example, you can run object detection models at amusement parks to keep track of visitor count. Cameras locate the visitors and maintain a running headcount locally without having to send massive amounts of video feed to the cloud, which is often a challenge due to limited internet bandwidth at parks. This solution can predict wait times at popular theme park rides and help improve the customer experience.
Security
Security camera manufacturers are looking for new ways to make devices more intelligent and automate their threat detection capabilities. AWS Greengrass ML Inference can help improve the capabilities of security cameras. Greengrass enabled cameras can continuously scan premises to look for a change in the scene, such as an incoming visitor, and send an alert. The cameras are able to quickly perform scene detection analysis locally and send data to the cloud only when required, e.g., for additional analysis to identify whether a visitor is a family member.
Precision Agriculture
The agriculture industry is going through two major disruptions. First, the world’s population continues to grow causing the demand for food to outweigh the output. Second, climate change is resulting in unpredictable weather conditions, affecting crop yields. AWS Greengrass ML Inference can help transform agriculture practices and deliver new value to customers. Greengrass-powered cameras installed in greenhouses and farms can process images of plants, crops, and data from sensors in the soil to not only detect environmental anomalies such as change in temperature, moisture, and nutrition level, but also trigger alerts.
Predictive Industrial Maintenance
As pricing pressure increases on manufacturers, they are looking for newer ways to help increase operational efficiency on factory floors. Delays in detecting issues on the manufacturing assembly line can lead to a waste of time and resources. AWS Greengrass ML Inference can help you in early detection of faulty equipment and issues on the factory floor. Greengrass-powered industrial gateways can continuously monitor the sensor data (e.g., vibrations, noise-level), predict anomalies, and take relevant actions such as send alerts or shut-off the power to minimize losses.