Tag: AWS IoT Greengrass

We are excited to highlight 60 AWS Partners that received new designations in December for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

Smart manufacturing and aftermarket services both require a new approach to connected device maintenance, recall, and warranty value chains. To really succeed, manufacturers need to automate the end-to-end processes connecting devices and users to the extended value chain. Learn how the seamless combination of Pegasystem’s low-code intelligent automation and AWS IoT can provide manufacturers with the most compelling digital transformation platform.

Customers across industries are looking to optimize business KPIs and outcomes throughout the product lifecycle. Enterprises can achieve this goal by using real-time insights from industrial assets provided by their digital twins, a unique data-driven approach has evolved to become a critical tool for accelerating enterprise digital transformation. Learn about the Atos Digital Twin Platform, its delivery methodology, the industry challenges it addresses, and the underlying reference architecture powered by AWS.

Within AWS IoT fleet deployments, each connected device needs to have unique, trusted, verifiable identity. While Linux OS provides strong protection from unauthorized user access, it’s still possible to compromise the system if the attacker has full physical access to the device. NXP EdgeLock SE050 prevents attackers from extracting the private key even if the physical integrity of the device is compromised and the attacker managed to get access to the device filesystem.

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.