Artificial Intelligence

In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). This post details the techniques behind these outcomes: from foundational methods like Supervised Fine-Tuning (SFT) (instruction tuning), and Proximal Policy Optimization (PPO), to Direct Preference Optimization (DPO) for human alignment, to cutting-edge reasoning optimizations such as Grouped-based Reinforcement Learning from Policy Optimization (GRPO), Direct Advantage Policy Optimization (DAPO), and Group Sequence Policy Optimization (GSPO) purpose-built for agentic systems.

Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements.

The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition.

In this post, we demonstrate how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation.

In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org.

This post introduces generative AI guided business reporting—with a focus on writing achievements & challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting.

In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails.

In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages.

In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents.

This post explores how new serverless model customization capabilities, elastic training, checkpointless training, and serverless MLflow work together to accelerate your AI development from months to days.