Amazon Web Services continued to expand its capabilities for powering internet of things applications today with a suite of new services on its cloud platform. Customers should be able to send machine learning models down to edge devices, secure those devices, manage them, and analyze the data coming off of them more readily as a result.

These services help Amazon capitalize on a growing set of workloads that are well-suited for integration with cloud computing. IoT workloads often require the management of large fleets of devices, plus the storage of massive (and constantly growing) amounts of data. Those requirements make cloud computing platforms like AWS well-suited to the task.

A new Greengrass ML Inference service makes it easier for customers to deploy trained machine learning models to edge devices, so that it’s possible to make intelligent decisions without access to the cloud. Greengrass is AWS’s platform for running code on remote devices, while managing that process through the cloud, and this new feature makes it easier to deploy intelligent algorithms to the edge.

That’s important for applications that require low-latency decision making, or situations where developers expect inconsistent network connectivity. For example, writing a machine learning model to run on an oil rig wouldn’t be as useful if it requires a persistent network connection. The new Greengrass ML Inference feature should make that more possible.

A trio of new services lets AWS customers manage and secure a network of IoT devices.

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