Category: Artificial Intelligence

Introduction Generative AI-powered chatbots are driving productivity gains across industries by providing instant access to information from diverse data sources, accelerating decision-making, and reducing response times. In fast-paced industrial environments, process engineers, reliability experts, and maintenance personnel require quick access to accurate, real-time operational data to make informed decisions and maintain optimal performance. However, querying […]

Introduction Building an IoT device for an edge Computer Vision and Machine Learning (CVML) solution can be a challenging undertaking. You need to compose your device software, ingest video and images, train your models, deploy them to the edge, and manage your device fleet remotely. This all needs to be performed at scale, and often […]

Introduction McKinsey research shows that a remarkable 70% of top tech executives are investing in digital twins, highlighting the growing momentum for this technology. Their goal is to achieve more agile and resilient operations, as digital twins offer a wide range of applications, from remote control and monitoring to complex system simulations, enabling a new […]

Although mostly invisible, embedded systems are ubiquitous in our everyday lives, powering applications, such as smart home automation or industrial control systems. At the upcoming Embedded World 2024 trade fair, Amazon Web Services (AWS) will showcase its latest innovations and solutions in the field of Internet of Things (IoT), tailored towards the embedded industry. Join us […]

Over the last few years, the industrial and manufacturing sectors have witnessed an accelerated transformation fueled by the advancement of the Industrial Internet of Things (IIoT), artificial intelligence (AI), and machine learning (ML). At the heart of this transformation is data, which when harnessed effectively, can propel businesses to new heights of operational efficiency, innovation, […]

Introduction Building a resilient path to post-pandemic recovery requires adaptability to dynamic trends. Therefore, many logistics leaders use predictive analytics to drive supply chain decisions, improve internal operational processes, meet regulatory compliance, and reduce transportation maintenance costs. Use cases for advanced predictive analytics in logistics include transportation management, fleet management, last-mile delivery, and visibility into […]

Introduction Customers in manufacturing, logistics, and energy sectors often have stringent requirements for needing to run machine learning (ML) models at the edge. Some of these requirements include low-latency processing, poor or no connectivity to the internet, and data security. For these customers, running ML processes at the edge offers many advantages over running them […]

AWS IoT VP, Yasser Alsaied, talks IoT strategy, commitment, and outlook Introduction The future of the Internet of Things (IoT) has been a topic of ongoing discussion and speculation. With implications for IoT hyperscalers, vendors, and customers alike, the landscape is rapidly evolving. To shed light on this topic, I spoke with Yasser Alsaied, vice […]

Post by Angela Wang and Tanner McRae, Senior Engineers on the AWS Solutions Architecture R&D and Innovation team This post is the third in a series on how to build and deploy a custom object detection model to the edge using Amazon SageMaker and AWS IoT Greengrass. In the previous 2 parts of the series, we walked […]

Introduction In working with customers, we often hear of a desired Digital Twin use case to drive actionable insights through what-if scenario analysis. These use cases typically include operations efficiency management, fleet management, failure predictions, and maintenance planning, to name a few. To help customers navigate this space, we developed a concise definition and four-level […]