Tag: Amazon Sagemaker

One of the primary strategies for growth for partners is through global expansion. Amazon Web Services (AWS) continues to invest in infrastructure around the globe. The Middle East is one region with huge potential for growth. According to IDC, more than 75 percent of workloads are on premises in the United Arab Emirates (UAE) and Saudi Arabia. The AWS and IDC report “Unlocking the Full Potential of AI in the Middle East” points out that 28 percent of organizations surveyed in the UAE and Saudi Arabia are currently investing in AI while another 50 percent plan to invest. Read this post to learn more.

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.

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.

In the age of generative artificial intelligence (AI), data isn’t just king—it’s the entire kingdom. Our previous blog post, Anduril unleashes the power of RAG with enterprise search chatbot Alfred on AWS, highlighted how Anduril Industries revolutionized enterprise search with Alfred, their innovative chat-based assistant powered by Retrieval-Augmented Generation (RAG) architecture. In this post, we examine the technical intricacies that make this system possible.

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.

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.

The American College of Radiology (ACR) is a nonprofit organization and world leader supporting the radiology community. Founded in 1923, ACR represents more than 41,000 diagnostic and interventional radiologists, radiation oncologists, nuclear medicine physicians, and medical physicists. In this post, we share how ACR implemented a Statistical Analysis System (SAS) solution on Amazon Web Services (AWS) to accomplish their big data processing and analysis needs, resulting in better cost optimization, performance improvements, and improved scalability.

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. Through this program, customers are making over 100 petabytes (PB) of high-value, cloud-optimized data available for public use. This quarter, AWS released 39 new or updated datasets.

At Amazon Web Services (AWS), we are committed to empowering our partners to accelerate their cloud innovation journeys. Our AWS Partner Transformation Program (PTP) is an important part of this commitment, offering comprehensive assessment, training, and enablement to help our public sector partners build successful and profitable AWS-based businesses.

Amazon Web Services (AWS) offers a variety of options for building chat-based assistants with generative artificial intelligence (AI) capabilities. The goal of this post is to present in simple words some of these options and what to keep in mind to decide which to use and how to get started.