Category: Amazon SageMaker

The Amazon Web Services (AWS) Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS. Through this program, customers are making more than 100 petabytes (PB) of high-value, cloud-optimized data available for public use. This past quarter, AWS released 21 new or updated datasets. What will you build with these datasets?

Cognitive Space is a leading Amazon Web Services (AWS) Partner delivering intelligent automation to satellite constellation operations using the CNTIENT platform. The system uses AWS-powered artificial intelligence (AI) decision making to handle highly complex and dynamic satellite tasking requirements, and demanding mission requirements. This blog post provides technical guidance for building and operating mission operation centers (MOCs) on AWS.

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.

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.

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.

Amazon SageMaker is a fully managed machine learning (ML) service that provides various capabilities, including Jupyter Notebook instances. While RStudio, a popular integrated development environment (IDE) for R, is available as a managed service in Amazon Web Services (AWS) commercial Regions, it’s currently not offered in AWS GovCloud (US) Regions. Read this post, however, to learn how you can use SageMaker notebook instances with the R kernel to perform data analytics tasks in AWS GovCloud (US) Regions.