Category: Amazon Machine Learning

Amazon Web Services (AWS) is investing $4 million to support the work of the HALO Trust and trial the use of artificial intelligence (AI) with drone imagery to locate minefields and other explosive remnants of war in Ukraine. Innovating with AWS will enable HALO to make wider use of the high-resolution drone footage it collects, including testing machine learning (ML) models for identifying mines.

Generative artificial intelligence (AI) innovation and inspiration dominated today’s AWS Summit Washington, DC keynote. But there was no shortage of newsworthy moments and key takeaways that extended beyond generative AI. Dave Levy, vice president of Worldwide Public Sector at Amazon Web Services (AWS), delivered the keynote and was joined onstage by three guest speakers who helped him set the tone for the annual two-day event that brings the public sector cloud community together in the nation’s capital.

Announced today by Amazon Web Services (AWS), the two-year, $50 million investment is designed to help public sector organizations – and those that directly support their technology needs – to accelerate innovation in support of critical missions using AWS generative AI services and infrastructure, such as Amazon Bedrock, Amazon Q, Amazon SageMaker, AWS HealthScribe, AWS Trainium, and AWS Inferentia.

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.

In March 2021, Amazon Web Services (AWS) launched the Think Big for Small Business (TBSB) Program, which provides partners with access to financial incentives and additional visibility with customers and the AWS team. Since the program launched, it has supported 1,000-plus partners from 73 countries across the globe. In this post, we explore the pivotal role that the TBSB program plays in accelerating the growth of small business partners that serve public sector customers.

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.

Government agencies are increasingly using large language models (LLMs) powered by generative artificial intelligence (AI) to extract valuable insights from their data in the Amazon Web Services (AWS) GovCloud (US) Regions. In this guide, we walk you through the process of adapting LLMs to specific domains with parameter efficient fine-tuning techniques made accessible through Amazon SageMaker integrations with Hugging Face.

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.

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.