AWS Greengrass is software that lets you run local compute , messaging, data caching, and sync 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. Now, with the AWS Greengrass Machine Learning (ML) Inference capability, you can also easily perform ML inference locally on connected devices.

Machine learning uses statistical algorithms that can learn from existing data, a process called training, in order to make decisions about new data, a process called inference. During training, patterns and relationships in the data are identified to build a model for decision making. This model allows a system to then make intelligent decisions about data it hasn’t encountered before. Training ML models requires massive computing resources, so it is a natural fit for the cloud. But, inference typically takes a lot less computing power and is often done in real-time when new data is available. So, getting inference results with very low latency is important to making sure your IoT applications can respond quickly to local events.

AWS Greengrass ML Inference gives you the best of both worlds. You use ML models that are built and trained in the cloud and you deploy and run ML inference locally on connected devices. For example, you can build a predictive model in Amazon SageMaker for scene detection analysis and then run it locally on a Greengrass enabled security camera device where there is no cloud connectivity to predict and send an alert when an incoming visitor is detected.

Benefits

Easily run ML Inference on Connected Devices

Performing inference locally on connected devices reduces the latency and cost of sending device data to the cloud to make a prediction. Rather than sending all data to the cloud for performing ML inference, Greengrass’s ML Inference capability enables you to run inference directly on the device. The data is sent to the cloud only when it requires additional processing.

Flexible

Greengrass ML Inference includes a pre-built TensorFlow, Apache MXNet, and Chainer package for all devices powered by Intel Atom, NVIDIA Jetson TX2, and Raspberry Pi. So, you don’t have to build and configure the ML framework for your devices from scratch. In addition to supporting TensorFlow, Apache MXNet, and Chainer, Greengrass ML also works with other popular frameworks including Caffe2 and the Microsoft Cognitive Toolkit. With Greengrass ML Inference, you also have the flexibility to build and train your ML model in Amazon SageMaker or to bring your own pre-trained model that is stored in Amazon S3.

Deploy Models to Your Connected Device with a Few Clicks

AWS Greengrass ML Inference makes it easy to deploy your machine learning model from the cloud to your devices. With just a few clicks in the Greengrass console, you can locate trained models in GG console, select the desired ML model, and deploy it to the target devices. Your models will be deployed and run on the connected device of your choice.

Accelerate Inference Performance with GPUs

AWS Greengrass ML Inference gives you access to hardware accelerators, such as GPUs on your devices, by including the accelerator device as a Greengrass local resource in the Greengrass console.

How It Works

Use Cases

Video Processing

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.

Featured Customers

Yanmar leverages AWS Greengrass ML Inference as part of their IoT precision agriculture solution that increases the intelligence of greenhouse operations by automatically detecting and recognizing the main growth stages of vegetables.

AWS Greengrass ML Inference enabled IoT devices allows DFDS to predict and optimize ship propulsion, ultimately reducing fuel consumption for their entire fleet.

AWS Greengrass ML Inference

Run machine learning models on AWS Greengrass devices