Tag: AWS IoT Greengrass
Drive Action with Automated Quality Inspections Using AWS IoT Greengrass 2.0, AWS IoT SiteWise, and ADLINK Edge
ADLINK’s machine vision platform can detect defects in products as they move along the conveyor line. AWS IoT Greengrass 2.0 provides the secure link between edge solutions and the cloud. The result is a secure system that enables rich and informative decision-making to allow AI-guided solutions, such as quality inspections of operations and production, as well as alerts when actions are required. At the edge, ADLINK monitors production line data in real-time.
To help safeguard workplaces from the pandemic, TensorIoT developed SafetyVisor, a suite of machine learning tools that can operate independently or in tandem with existing business infrastructure to monitor safety gear usage (like masks) and social distancing. SafetyVisor’s computer vision models are designed to work with your existing cameras, and the entire solution is built utilizing a flexible architecture to facilitate easy deployment and use.
You can manage agents in a multi-tenant SaaS environment using AWS IoT Core. Review a solution that offers a modular and secure approach to register and manage agents. It relies on AWS managed services to offload the heavy lifting of implementing and maintaining those mechanisms, and provides scalability, elasticity, and availability. Using AWS IoT Core for agent management provides various capabilities for managing, securing, and analyzing usage and sent data from the registered agents.
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 machine learning journey using AWS. Learn how to build a simple AI-enabled application with AWS IoT Greengrass that takes advantage of the hardware AI acceleration on the LG AIoT board. AWS IoT Greengrass extends AWS on your device and offers the cloud programming model and tools at the edge.
Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.
To receive the AWS Service Delivery designation, organizations must undergo rigorous technical validation. They are also assessed on the security, performance, and reliability of their AWS solutions. To help APN Consulting Partners better understand this process and our validation requirements, we are releasing new versions of the AWS Service Delivery Validation Checklists. These outline for the customer case study and technical criteria needed to achieve the AWS Service Delivery designation.
Connected devices require a complex combination of hardware, operating systems, and software to connect to the cloud. To simplify this development for customers, AWS works with Independent Hardware Vendors (IHV) that can integrate AWS IoT services. In this post, we explore three qualified AWS IoT Edge Software offerings, and dive deep on how these IHVs solve business use cases while enabling customers to use these software frameworks to connect to AWS IoT services.
Advances in the Industrial Internet of Things (IIoT) have made smart factories a reality through the application of AI and cloud computing technologies. In this post, explore how EXOR International’s systems-on-module (SOM) and edge gateways, powered by Intel’s Cyclone V FPGA, allow system integrators and application builders to deliver AWS-based IIoT solutions with faster time-to-market, lower total cost of ownership, and reduced development efforts.
With connected IoT solutions built on AWS, businesses can be more proactive with maintenance instead of reactionary, allowing them to fix problems with machinery before they become critical. Reliance Steel & Aluminum Co. teamed up with TensorIoT to solve for this use case. Together, they built an IoT solution on AWS that ensures the maintenance needs of Reliance’s industrial machinery are anticipated and that machines can be serviced before breaking down.
The ultimate potential of IoT will only be achieved if the security of such a vastly powerful and complex system can be maintained. Doing so requires security implementations to be simple and mainstream. Microchip Technology is a leading provider of microcontroller and analog semiconductors, providing low-risk product development, lower total system cost, and faster time to market for thousands of diverse customer applications worldwide.