Category: Analytics

Higher education institutions are under increasing pressure to demonstrate the effectiveness of their programs and provide students with a clear path to degree completion. Data analytics can help these institutions proactively identify and support at-risk students, allowing them to develop personalized intervention strategies to improve student retention and graduation rates. In this post, we’ll explore how Amazon Redshift ML, a powerful machine learning (ML) capability within the Amazon Redshift data warehouse, can enable higher education leaders to quickly predict student outcomes and communicate insights to key stakeholders.

The AWS Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on Amazon Web Services (AWS). AWS works with data providers to democratize access to data by making it available to the public for analysis on AWS; develop new cloud-based techniques, formats, and tools that lower the cost of working with data; and encourage the development of communities that benefit from access to shared datasets. The full list of publicly available datasets are on the Registry of Open Data on AWS and are now also discoverable on AWS Data Exchange. This quarter, AWS released 53 new or updated datasets and you can learn more about them in this post.

This post explores the architectural design and security concepts employed by Radboud University Medical Center Nijmegen (Radboudumc) to build a secure artificial intelligence (AI) runtime environment on Amazon Web Services (AWS). Business leaders dealing with sensitive or regulated data will find this post invaluable because it demonstrates a proven approach to using the power of AI while maintaining strict data privacy and security standards.

Organizations require solutions for real time or near real time dashboards that can be provided to their customers without impacting their database performance or service level agreements (SLAs) to their end users. In this post, we showcase the integration of Amazon Web Services (AWS) capabilities to present an end-to-end architecture for this data flow.

Across NATO’s 32 member states, today’s rapidly evolving threat landscape requires continuous modernization of advanced technology solutions, underscoring the strategic importance of the alliance’s digital transformation. This modernization effort demands speed, scale, security, and global innovation capabilities to stay ahead. Collaborating with technology leaders like Amazon Web Services (AWS) can accelerate innovation and NATO’s ability to deliver mission-ready solutions to counter known and emerging threats.

Global leaders are convening in Germany this week at the annual Munich Security Conference (MSC) to discuss key foreign policy and security challenges. Amid these discussions, one thing is clear: global leadership increasingly hinges on technological progress. In particular, the rapid progress of artificial intelligence (AI) presents an extraordinary opportunity for transformative innovation. For the U.S. government, this moment marks a crucial inflection point: embrace bold modernization of digital infrastructure and AI investment or risk being outpaced by global competitors.

In an earlier post, Build secure and scalable data platforms for the European Health Data Space (EHDS) with AWS, we discussed a reference architecture for building secure and scalable data platforms for secondary usage of health data in alignment with the European Health Data Space (EHDS) using Amazon Web Services (AWS). This follow-up post walks you through the technical implementation details for building such federated data governance and analysis platforms using AWS. Whether you are a healthcare organization, technology provider, or systems integrator, this post aims to equip you with the technical knowledge to build a secure data platform for secondary usage of health data in alignment with the EHDS Regulation.

Sharing data is essential for organizations to make informed decisions and foster innovation. Amazon Web Services (AWS) offers a variety of tools and services to help distribute data securely and at scale. Whether it’s sharing public data for the common good, monetizing private datasets for business purposes, or collaborating with colleagues, AWS provides the necessary infrastructure and support. Read this post to learn more.

Today, generative artificial intelligence (AI) is at the core of many of the decisions and technologies businesses across the globe are implementing. The linchpin to ensuring that generative AI is effective depends on the foundation models (FMs) storing the data. To make sure that those FMs are pulling the correct data, Amazon Web Services (AWS) has developed a solution through Amazon Bedrock to generate SQL to answer user questions about the data. The solution was developed to support the Federal Institute of São Paulo (IFSP) and has optimized their decision-making.

In this post, we explore how generative artificial intelligence (AI), powered by services such as Amazon Bedrock and Amazon SageMaker, can be harnessed to meet the unique challenges of AWS GovCloud (US). We highlight use cases that demonstrate the potential of generative AI to enhance efficiency, automate workflows, and extract insights—all within a secure, compliant framework.