Tag: best practices

The Amazon Web Services (AWS) commitment to safe, transparent, and responsible artificial intelligence (AI)—including generative AI—is reflected in our endorsement of the White House Voluntary AI Commitments, our participation in the UK AI Safety Summit, and our dedication to providing customers with features that address specific challenges in this space. In this post, we explore how AWS can help agencies address the governance requirements outlined in the Office of Management and Budget (OMB) memo M-2410 as public sector entities look to build internal capacity for AI.

Governments encourage public sector organizations, businesses, and citizens to embrace digital technologies and practices through a range of incentives. These incentives standardize processes and motivate behaviors that achieve the objectives of these initiatives in a way that can be sustained over time. This post, written by an Amazon Web Services (AWS) government transformation advisor, highlights 10 ways that governments use incentives to accelerate successful digital transformation. 

Generative artificial intelligence (AI) is revolutionizing public agencies by streamlining services and providing valuable insights from large datasets. However, adding generative AI to your agency is not a simple process. SMX, an Amazon Web Services (AWS) Premier Tier Services Partner, helped one nonprofit agency build a robust architecture in the AWS Cloud that provided them the foundation for building and implementing generative AI tools. In this guest post, experts from SMX explain five best practices they used to help this agency prepare for generative AI.

In this post, we will explore the use of generative artificial intelligence (AI) chatbots as a natural language alternative to the service catalog approach. We will present an Amazon Web Services (AWS) architecture pattern to deploy an AI chatbot that can understand user requests in natural language and provide interactive responses to user requests, directing them to the specific systems or services they are looking for. Chatbots simplify the content navigation and discovery process while improving the customer experience.

K12 leaders need tangible solutions and tactics for improving their school’s or district’s cyber-resilience in the coming school year, and Amazon Web Services (AWS) is committed to supporting schools and districts as they enhance the cybersecurity of their networks. Recently, AWS joined the White House, the Department of Homeland Security, and the Department of Education—among other leaders in the government and education community—to commit to improving the cybersecurity resilience of K12 education. As part of this commitment, AWS created the K12 Cyber Grant Program, offering up to $20 million in AWS Promotional Credits to both new and existing K12 customers.

The Amazon Web Services (AWS) Cloud Maturity Assessment (CMA) uses the AWS Cloud Adoption Framework (AWS CAF) to assess the maturity of your cloud adoption, and provide prescriptive guidance on prioritized next steps. By replacing guesswork with data-driven analysis, you will get a blueprint to prioritize, build, and mature your cloud capabilities. Read this post to learn more.

Amazon Web Services (AWS) customers, technology leaders, and government agency CIOs continually balance risk and reward to accomplish many things by using AWS GovCloud (US). They work to transform citizen experiences, save taxpayers money, support sustainable growth, expand market presence, modernize legacy systems, increase security, drive efficiencies, strengthen brand equity, and reduce environmental impact.

Healthcare organizations invest heavily in technology and data. Generative artificial intelligence (AI) empowers healthcare organizations to leverage their investments in robust data foundations, improve patient experience through innovative interactive technologies, boost productivity to help address workforce challenges, and drive new insights to accelerate research. This post highlights three examples of how generative AI on Amazon Web Services (AWS) is being used in healthcare and discusses ways to leverage this technology in a responsible, safe way.

Amazon Web Services (AWS) works with higher education institutions, research labs, and researchers around the world to offer cost-effective, scalable, and secure compute, storage, and database capabilities to accelerate time to science. In our work with research leaders and stakeholders, users often ask us about best practices for leveraging cloud for research. In this post, we dive into five common questions we field from research leaders as they build the academic research innovation centers of the future.

The first blog post in this series outlined considerations for developing API pipelines on AWS to extract data from third-party SaaS tools. With consolidated data, public sector organizations can offer new experiences for donors and members, enrich research datasets, and improve operational efficiency for their staff. This follow-up blog post presents options for ingesting SaaS data with API requests from AWS services, methods for handling payload data from API calls, and guidance for orchestration and scaling an API data pipeline on AWS.