Official technical community of Amazon Cloud. Shares practical AI/ML, big data, database, modern app development, IoT content, offers comprehensive learning resources, hosts regular developer events, and continuously empowers developers.
DeepSeek‑V3.1 arrives on Amazon Bedrock as a fully managed foundation model offering two inference modes, improved benchmark performance over DeepSeek‑R1, support for over 100 languages, enhanced tool‑calling and agent capabilities, and detailed guidance for secure enterprise deployment.
The article explains how the limited context window of large language models causes prompt bloat when many tool descriptions are embedded, and presents the RAG‑MCP architecture that stores tool metadata in a vector database, uses semantic retrieval to select only the most relevant tools, dramatically shortens prompts, and improves inference speed and tool‑call accuracy.
This article walks through a complete, low‑cost SEO pipeline built on Amazon Bedrock’s generative AI, covering automated keyword extraction, search‑intent analysis, product‑vector matching with FAISS, and AI‑driven content creation within a serverless, event‑driven architecture that scales efficiently while reducing manual effort.
This guide shows how to fine‑tune the Amazon Nova Canvas foundation model on Amazon Bedrock using a 12‑hour workflow that extracts character frames from video with Amazon Rekognition, prepares labeled data, configures hyper‑parameters, creates a custom model, deploys it with provisioned throughput, and tests the model to generate coherent storyboard images, while also covering cleanup steps to avoid ongoing costs.
The article explains how to maintain character consistency and visual style across AI‑generated storyboard scenes by using structured prompt engineering, seed and cfgScale parameters, and a workflow that combines Amazon Nova Lite, Canvas, and Reel, while also showing concrete examples and limitations.
The article explains why data quality is critical for modern businesses, outlines the challenges data engineers face when monitoring hundreds of models, and provides a step‑by‑step guide to constructing an automated, AI‑driven data quality monitoring system using Strands Agents, Amazon Bedrock, dbt and Amazon Redshift, including code examples, workflow orchestration, security hardening, containerized deployment and observability.
This article demonstrates how to use Amazon Q CLI's natural‑language interface to dissect the full network path from an ALB through EC2 node iptables to a pod in an Amazon EKS cluster, then examines four typical networking problems and provides concrete mitigation strategies.
Amazon Finance built an AI assistant that combines Amazon Bedrock, Claude 3 Sonnet, and Amazon Kendra to let analysts query financial data in natural language, achieving a 30% reduction in search time, an 80% increase in accuracy, and high precision and recall across data‑discovery and document‑search tasks.
