Category: Generative AI

Modernize applications at scale with this new AI-powered service that learns your organization’s patterns, automates transformations across repositories, and cuts execution time by up to 80% through both pre-built and custom capabilities.

Speed up AWS development with an open source tool that analyzes your code to generate valid IAM policies, providing AI coding assistants with up-to-date AWS service knowledge and reliable permission recommendations.

Amazon Nova Multimodal Embeddings is a new state-of-the-art embedding model available in Amazon Bedrock. It is the industry’s first embedding model that supports text, documents, images, video, and audio through a single unified model, enabling cross-modal retrieval and semantic search applications with leading accuracy, at industry-leading costs.

Amazon has announced Quick Suite, a new AI-powered workspace that combines research, business intelligence, and automation tools to help users analyze data and streamline workflows all in one place.

Summer has drawn to a close here in Utrecht, where I live in the Netherlands. In two weeks, I’ll be attending AWS Community Day 2025, hosted at the Kinepolis Jaarbeurs Utrecht on September 24. The single-day event will bring together over 500 cloud practitioners from across the Netherlands, featuring 25 breakout sessions across five technical […]

Build responsible AI applications with the first and only solution that delivers up to 99% verification accuracy using sound mathematical logic and formal verification techniques to minimize AI hallucinations and data ambiguity.

AWS AI league is a program that helps organizations upskill their workforce by combining fun competition with hands-on learning using AWS AI services. It offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization, and prompt engineering – essential skills for building generative AI solutions.

AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback and model distillation to better address domain-specific requirements across industries.