Tag: AWS CloudFormation

Citizens are increasingly expecting government to provide modern digital experiences for conducting online transactions. Market research tells us 63 percent of consumers see personalization as the standard level of service. This post offers various architectural patterns for improving customer experience for the public sector for a wide range of use cases. The aim of the post is to help public sector organizations create customer experience solutions on the Amazon Web Services (AWS) Cloud using AWS artificial intelligence (AI) services and AWS purpose-built data analytics services.

In this post, we explore the complexities of data privacy and controls on Amazon Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

The Natural History Museum in London is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have worked together to transform and accelerate scientific research by bringing together a broad range of UK biodiversity and environmental data types in one place for the first time. In this post, the first in a two-part series, we provide an overview of the NHM-AWS project and the potential research benefits.

In this post, you will learn how to deploy Amazon Web Services (AWS) Cloud Intelligence Dashboards (CID) using the Landing Zone Accelerator on AWS (LZA) solution. In doing so, you will learn how to customize your LZA deployment using the customizations-config.yaml file. By utilizing the LZA and CID together, you can streamline the deployment process, ensure compliance with best practices, and gain valuable insights into your cloud environment, ultimately leading to improved operational efficiency, enhanced security, and better-informed decision-making.

Digital engineering fundamentally relies on integrating data across model structures by using a digital thread – an underlying framework for integrating data from across traditionally siloed functions that create a consolidated view of the system’s data throughout its lifecycle. The cloud is integral to digital engineering by supporting collaboration across geographically dispersed organizations, automating workflows for data connectivity and trade space analysis in a reliable, scalable, and cost-effective manner. This post describes how Amazon Web Services (AWS) Partner General Dynamics Information Technology (GDIT) has used digital engineering in combination with secure and scalable AWS services, to deliver secure IT systems to a large defense program.

Amazon Web Services (AWS) developed the Data Fabric Security (DFS) on AWS solution to support the identity and access needs of a multi-organization system. With DFS on AWS, federal customers can accelerate joint interoperability, modernization, and data-driven decision making in the cloud by removing barriers that prevent systems and users from communicating while still strengthening security via Zero Trust principles.

Nonprofit organizations focus on a specific mission to impact their members, communities, and the world. In the nonprofit space, where resources are limited, it’s important to optimize the impact of your efforts. Learn how you can apply machine learning with Amazon Redshift ML on public datasets to support data-driven decisions optimizing your impact. This walkthrough focuses on the use case for how to use open data to support food security programming, but this solution can be applied to many other initiatives in the nonprofit space.

Many organizations perform direct mailings, designed to support fundraising or assist with other efforts to help further the organization’s mission. Direct mailing workflows can use everything from a Microsoft Word mail merge to utilizing a third-party mailing provider. By leveraging the power of the cloud, organizations can take advantage of capabilities that might otherwise be out of reach, like customized personalization at scale. In this walkthrough, learn how organizations can utilize machine learning (ML) personalization techniques with AWS to help drive better outcomes on their direct mailing efforts.

Satellite communications (satcom) networks typically offer a rich set of performance metrics, such as signal-to-noise ratio (SNR) and bandwidth delivered by remote terminals on land, sea, or air. Customers can use performance metrics to detect network and terminal anomalies and identify trends to impact business outcomes. This walkthrough presents an approach using serverless resources from AWS to build satcom control plane analytics pipelines. The presented architecture transforms the data to extract key performance indicators (KPIs) of interest, renders them in business intelligence tools, and applies machine learning (ML) to flag unexpected SNR deviations.

Local public health departments must notify public health agencies, like state health departments or the Centers for Disease Control and Prevention (CDC), of reportable conditions. These departments receive various types of reports of healthcare conditions through email, in addition to more traditional methods such as mail, fax, or phone calls. Local health departments can dramatically reduce the time and costs associated with manually processing email attachments and improve processing efficiency using automation. In this blog post, learn how to create an automated email attachment ingestion, storage, and processing solution powered by artificial intelligence (AI) and machine learning (ML) services from AWS.