Tag: aws iot greengrass

Empowering Smarter Decisions at the Edge In today’s data-driven world, businesses must deliver insights faster, enhance customer experiences, and improve efficiency. Traditional data processing often falls short of meeting real-time decision-making needs. In a manufacturing plant, sensor data can detect machine deterioration, but traditional cloud-based data analysis may not generate insights fast enough to prevent […]

Introduction Water meters are present at almost every location that consumes water, such as residential houses or large-scale production plants. Avoiding water loss is increasingly important as water shortages are more frequent across all continents. Due to an aging infrastructure, 30% of water flowing through pipes is lost to leaks (AWS announces 6 new projects […]

Introduction Internet of Things (IoT) developers often need to implement a robust mechanism for managing IoT device state either locally or remotely. A typical example is a smart home radiator, an IoT device where you can use the built-in control panel to adjust the temperature (device state), or trigger temperature adjustment messages remotely from a […]

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 this post, we will be discussing how to manage Docker container lifecycle using an AWS IoT Greengrass custom component. There are five phases in a Docker container lifecycle: create, run, pause/unpause, stop, and kill. The custom component interacts with the Docker Engine via the Docker SDK for Python to manage processes based on […]

U.S. prescription medications costs are approaching $500 billion a year and growing up to 7% annually, according to a House Ways and Means Committee report. In this market, billions of dollars in unused medicines are still wasted annually due to traditional packaging that usually contains more pills or tablets than those prescribed by physicians. Automated pill […]

Introduction Industrial digital transformation can increase competitiveness and optimize processes and profitability through the use of big data, IoT, machine-to-machine communication, and machine learning. Continuous digitalization and progressive interconnectivity of the production environment is important to capturing value from industrial IoT (IIoT) solutions. While this new and expanding “physical meets digital” connectivity enables great rewards, […]

This post was co-authored by Praveen Rao, Principal GTM leader, Mfg and Supply Chain at AWS; Adrian Weiss, Product Manager and Senior Data Scientist at nexineer; Nicolas Byl, Director Software Engineering at nexineer; Jill Mannaioni, Vice President Americas at CloudRail; and Philip Weber, Senior Partner Manager, EMEA and APJ at CloudRail. Every manufacturing customer these […]

Introduction Edge computing, also known as fog computing and mobile computing, is a computing model that brings processing and data closer to the customer. By moving data closer to the customer, organizations need to review, and possibly expand, their security controls to ensure that their data is protected. In this blog I want focus on […]

Are you seeking ways to get an immersive 3D view of your systems and operations to optimize efficiency, increase production, and improve performance? Perhaps you are generating all the data you need from various on-premise systems, but are unsure how to gain access to this information in a living virtual representation. In this blog post, […]