Category: Artificial Intelligence

To be successful with a graph database—such as Amazon Neptune, a managed graph database service—you need a graph data model that captures the data you need and can answer your questions efficiently. Building that model is an iterative process. The earliest stage of the process, in which you are merely getting initial elements on paper […]

Generative AI is a form of artificial intelligence (AI) that is designed to generate content, including text, images, video, and music. In today’s business landscape, harnessing the potential of generative AI has become essential to remain competitive. Foundation models are a form of generative AI. They generate output from one or more inputs (prompts) in […]

The financial landscape has been in a constant state of evolution. From stock ticker machines to algorithmic trading systems, innovation has always been at the core of finance. Yet, among these transformative changes, the confluence of artificial intelligence (AI) and digital assets like cryptocurrencies, central bank digital currencies (CBDCs), and tokenized assets has the potential […]

Generative AI has captured our imagination and is transforming industries with its ability to answer questions, write stories, create art, and even generate code. AWS customers are increasingly asking us how they can best take advantage of generative AI in their own businesses. Most have accumulated a wealth of domain-specific data (financial records, health records, […]

Organizations across diverse sectors are exploring novel ways to enhance user experiences by harnessing the potential of Generative AI and large language models (LLMs). In the fashion industry generative AI is revolutionizing the creative process. By analyzing user preferences and data, AI algorithms can generate unique apparel patterns and designs, bringing a new level of […]

A knowledge graph allows us to combine data from different sources to gain a better understanding of a specific problem domain. In this post, we use Amazon Neptune (a managed graph database service) to create a knowledge graph about technology products. In addition to the data we already have in the graph, we add the […]

Organizations are looking for ways to better leverage their data to improve their business operations. With Amazon Aurora, Aurora Machine Learning, and Amazon SageMaker, you can train machine learning (ML) services quickly and directly integrate the ML model with your existing Aurora data to better serve your customers. In this post, we demonstrate how a […]

Businesses today want to enhance the data stored in their relational databases and incorporate up-to-the-minute predictions from machine learning (ML) models. However, most ML processing is done offline in separate systems, resulting in delays in receiving ML inferences for use in applications. AWS wants to make it efficient to incorporate real-time model inferences in your […]

This guest post is co-authored by Ryota Shima, Application Architect, and Kazuki Matsumura, Lead Architect at NRI Digital. NRI Digital has a wide variety of systems in production, both on-premises and cloud-based. Among them, many systems are built on AWS, and Amazon Aurora and Amazon Relational Database Service (Amazon RDS) are often used as the […]

Amazon DevOps Guru for RDS is a new capability for Amazon DevOps Guru that helps developers using Amazon Aurora database instances detect, diagnose, and resolve database performance issues fast and at scale. DevOps Guru for RDS uses machine learning (ML) to automatically identify and analyze a wide range of performance-related database issues, such as over-utilization […]