Machine Learning at the Edge with AWS Greengrass

In manufacturing, efficiency, accuracy, and speed are of paramount importance. To maximize these, manufacturers are increasingly turning to innovative technologies like machine learning (ML) and edge computing. AWS Greengrass is a software solution combining these technologies, and is offered by Amazon Web Services (AWS).

AWS Greengrass extends cloud capabilities to edge devices, enabling them to act locally on the data they generate while still using the cloud for management, analytics, and durable storage. It allows manufacturers to use machine learning at the edge, meaning they can run ML models on the devices themselves, rather than sending data back to a central server for processing.

Reduced Latency

Using ML at the edge reduces the latency occurring from sending data back and forth between devices and the cloud. This is particularly beneficial in manufacturing environments where real-time decision making is crucial. For instance, an ML model running on a device in a production line can instantly detect anomalies and trigger immediate actions—such as shutting down a machine before it damages the entire production run or batch.

Significantly Reduced Costs

Sending large volumes of data to the cloud for processing can be expensive. By processing data locally on a device, manufacturing companies can minimize the amount of data sent to the cloud, thereby reducing costs.

Improved Data Security

ML at the edge can improve data security. By processing data locally, sensitive information can simply stay on that device, reducing the risk of data breaches.

AWS Greengrass takes these benefits a step further by seamlessly integrating with other AWS services. For example, you can use Amazon SageMaker to build, train, and deploy ML models. Once the model is ready, you can deploy it to your local devices using AWS Greengrass. Now, your edge computing devices can make predictions locally and take action in real-time.

AWS Greengrass also provides a local runtime environment so that manufacturing companies can write AWS Lambda functions in the cloud and deploy them to their own local devices. These functions can be triggered by local events, timers, or other conditions, enabling you to automate a wide range of tasks.

Wrapping Up

Machine learning at the edge powered by AWS Greengrass offers a powerful solution for manufacturing companies that want to improve efficiency, reduce costs, and enhance data security. By enabling devices to act locally on production data, manufacturers can make real-time decisions, automate tasks, and minimize the amount (and the expense) of data sent to the cloud. As the manufacturing industry continues to evolve, solutions like AWS Greengrass will play a pivotal role in driving innovation and productivity.

Are you ready to deploy the power of AWS Greengrass and machine learning at the edge? Be sure to let us know the details of your project so we can help, or schedule a meeting with Cody Johnson in sales to get started right away.