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.