Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Edge computing is the act of running workloads on these edge devices. Machine learning at the edge (ML@Edge) is a concept that brings the capability of running ML models locally to edge devices. These ML models can then be invoked by the edge application. ML@Edge is important for many scenarios where raw data is collected from sources far from the cloud. Although ML@Edge can address many use cases, there are complex architectural challenges that need to be solved in order to have a secure, robust, and reliable design.

Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available. Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale.

You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these devices, deploying and managing ML models at the edge is often difficult. With AWS IoT Greengrass and Amazon SageMaker Edge Manager, you can perform ML inference on locally generated data on Snowball Edge devices using cloud-trained ML models. You not only benefit from the low latency and cost savings of running local inference, but also reduce the time and effort required to get ML models to production. You can do all this while continuously monitoring and improving model quality across your Snowball Edge device fleet.

Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.

AWS IoT Greengrass is software that extends cloud capabilities to local devices. This enables devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks. Local devices can also communicate securely with AWS IoT Core and export IoT data to the AWS Cloud. AWS IoT Greengrass developers can use AWS Lambda functions and prebuilt connectors to create serverless applications that are deployed to devices for local execution. The following diagram shows the basic architecture of AWS IoT Greengrass. AWS IoT Greengrass makes it possible for customers to build IoT devices and application logic. Specifically, AWS IoT Greengrass provides cloud-based management of application logic that runs on devices. Locally deployed Lambda functions and connectors are triggered by local events, messages from the cloud, or other sources. In AWS IoT Greengrass, devices securely communicate on a local network and exchange messages with each other without having to connect to the cloud. AWS IoT Greengrass provides a local pub/sub message manager that can intelligently buffer messages if connectivity is lost so that inbound and outbound messages to the cloud are preserved. Through secure connectivity in the local network. Device security credentials function in a group until they are revoked, even if connectivity to the cloud is disrupted, so that the devices can continue to securely communicate locally. MQTT messaging over the local network between devices, connectors, and Lambda functions using managed subscriptions. MQTT messaging between AWS IoT and devices, connectors, and Lambda functions using managed subscriptions. Shadows can be configured to sync with the AWS Cloud. Automatic IP address detection that enables devices to discover the Greengrass core device. Central deployment of new or updated group configuration.

With so many cloud applications infused with artificial intelligence (AI) and machine learning (ML) capabilities, AI/ML is being democratized by cloud services. The growth of AI in a wide range of applications demands more purpose-built processors to provide scalable levels of performance, flexibility, and efficiency. The LG AIoT board helps customers accelerate their computer vision and ML journey using Amazon Web Services (AWS). OEMs can now easily incorporate visual intelligence, voice intelligence, and control intelligence into their products. The LG Neural Engine (LNE) in the LG AIoT board offloads the compute requirements of deep learning algorithms to the specially designed processor, which delivers 1 TFLOPS of compute performance.