Category: Technical How-to

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in HAQM Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by HAQM Bedrock for summarization and redaction of sensitive information.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on HAQM SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use HAQM OpenSearch Serverless with its vector engine for low-latency semantic video search.

This post evaluates the key options for querying data using generative AI, discusses their strengths and limitations, and demonstrates why Text-to-SQL is the best choice for deterministic, schema-specific tasks. We show how to effectively use Text-to-SQL using HAQM Nova, a foundation model (FM) available in HAQM Bedrock, to derive precise and reliable answers from your data.

In this post, we’ll explore how to combine HAQM Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now generally available. This powerful combination transforms your development workflow by delivering lightning-fast responses from reducing inference response latency, as well as lowering input token costs.

We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4 and HAQM Nova on HAQM Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work […]

In this post, we explore how to use Graph-based Retrieval-Augmented Generation (GraphRAG) in HAQM Bedrock Knowledge Bases to build intelligent applications. Unlike traditional vector search, which retrieves documents based on similarity scores, knowledge graphs encode relationships between entities, allowing large language models (LLMs) to retrieve information with context-aware reasoning.

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using HAQM Bedrock to identify relationships between regulation changes and SOPs.

In this post you define, deploy, and provision a SageMaker Project custom template purely in Terraform. With no dependencies on other IaC tools, you can now enable SageMaker Projects strictly within your Terraform Enterprise infrastructure.

OpenSearch offers a wide range of third-party machine learning (ML) connectors to support this augmentation. This post highlights two of these third-party ML connectors. The first connector we demonstrate is the HAQM Comprehend connector. In this post, we show you how to use this connector to invoke the LangDetect API to detect the languages of ingested documents. The second connector we demonstrate is the HAQM Bedrock connector to invoke the HAQM Titan Text Embeddings v2 model so that you can create embeddings from ingested documents and perform semantic search.

HAQM Bedrock Model Copy and Model Share features provide a powerful option for managing the lifecycle of an AI application from development to production. In this comprehensive blog post, we’ll dive deep into the Model Share and Model Copy features, exploring their functionalities, benefits, and practical applications in a typical development-to-production scenario.