Category: Amazon SageMaker

Generative artificial intelligence (AI) has revolutionized how we work and is reshaping the workforce of the future. However, a recent study conducted by Access Partnership for AWS revealed that 81 percent of Malaysia’s employers struggle to find the AI talent they need, despite nine in 10 stating that hiring AI-skilled talent is a priority for them. To address the nation’s AI skills gap, AWS and Jabatan Pendidikan Politeknik & Kolej Komuni (JPPKK), an agency under Malaysia’s Ministry of Higher Education (MOHE), teamed up to introduce new training initiatives targeting both educators and students to uplift Malaysia’s TVET (technical and vocational education and training) community.

In Modernizing tax systems with AWS, we briefly touched upon infrastructure and application modernization using microservices and serverless architectures. We hear from multiple tax and labor agencies about their desire to move to API-based architectures and adopt new technologies. In this post, we dive deeper into these areas and discuss benefits, approaches, and best practices for building modern tax and unemployment insurance (UI) applications using microservices.

In January 2020, Amazon Web Services (AWS) inaugurated a Cloud Innovation Centre (CIC) at the University of British Columbia (UBC). The CIC uses emerging technologies to solve real-world problems and has produced more than 50 prototypes in sectors like healthcare, education, and research. The Centre’s work has involved 300-plus AWS accounts across various groups, including external collaborators, UBC staff, students, and researchers. This post discusses the management of AWS in higher education institutions, emphasizing governance to securely foster innovation without compromising security and detailing policies and responsibilities for managing AWS accounts across projects and research.

Pathologists have been looking at morphological patterns in patients’ tissue sections highlighted by hematoxylin and eosin (H&E) staining for more than a century. However, as the pathology transformation from glass slides to digital imaging gains momentum, it opens the door to artificial intelligence (AI) tools to complement expert assessment with quantitative measurements to enable data-driven medicine. Yet, challenges remain with handling digital imaging files such as storage and pre-processing prior to application of AI tools. Genomics England have utilised Amazon Web Services (AWS) and tools such as Amazon SageMaker to demonstrate how to prepare digital pathology images for research and the development of machine learning models.

The Natural History Museum (NHM) in London is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have partnered up to transform and accelerate scientific research by bringing together a broad range of biodiversity and environmental data types in one place for the first time. In an earlier post, we discussed NHM’s overall vision for using open data in combination with large-scale compute, data systems, and machine learning (ML) to create the Planetary Knowledge Base (PKB), a knowledge graph of global biodiversity. In this post, we focus on the underlying services and architecture that comprise the PKB.

Anduril Industries, a defense technology company, has launched Alfred, an internal enterprise search chatbot powered by cutting-edge Retrieval-Augmented Generation (RAG) architecture. By using Amazon Web Services (AWS) services, such as Amazon SageMaker, Amazon Kendra, and Amazon DynamoDB, on the secure AWS GovCloud (US) Regions, Anduril has built a robust and scalable data infrastructure that can support Alfred’s growing knowledge needs.

As Australia moves forward with a national framework for the assurance of artificial intelligence (AI) in government, Amazon Web Services (AWS) is committed to helping our customers implement AI solutions that align with Australia’s AI Ethics Principles. This post outlines how AWS tools and services can support government agencies in adhering to Australia’s AI Ethics Principles when developing AI and machine learning (ML) solutions. The post includes a focus on implementation to help Australian governments responsibly innovate whilst maintaining cloud-based agility.

A flexible architecture is a crucial factor in unlocking the full potential of generative artificial intelligence (AI) solutions. In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different open source foundation models in a flexible way. This solution provides several benefits.

Machine learning (ML) and artificial intelligence (AI) are transformative technologies, enabling organizations of all sizes to further their mission in ways not previously possible. But, it is critical to think responsibly about these technologies so that all users are treated fairly, data is appropriately protected, and individuals can make informed choices about consent. In this post, we discuss responsible AI and how you should think about your workloads. This approach will help ensure your AI systems are fair, transparent, and secure.

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?