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 Greengrass Machine Learning (ML) Inference capability, you can also easily perform ML inference locally on connected devices.
Machine Learning works by using powerful algorithms to discover patterns in data and construct complex mathematical models using these patterns. Once the model is built, you perform inference by applying new data to the trained model to make predictions for your application. Building and training ML models requires massive computing resources so it is a natural fit for the cloud. But, inference takes a lot less compute power and is typically done in real-time when new data is available, so getting inference results with very low latency is important to making sure your 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 the cloud about the diamond heads of boring equipment and then run it underground where there is no cloud connectivity to predict the wear and usage of the diamond.