Category: Artificial Intelligence

In this post, we demonstrate how to use SageMaker Unified Studio and AWS Identity and Access Management (IAM) to establish a robust permission framework for Amazon Bedrock models. We show how administrators can precisely manage which users and teams have access to specific models within a secure, collaborative environment. We guide you through creating granular permissions to control model access, with code examples for common enterprise governance scenarios.

This post demonstrates how integrating an Amazon Bedrock streaming API with AWS AppSync subscriptions significantly enhances AI assistant responsiveness and user satisfaction. By implementing this streaming approach, the global financial services organization reduced initial response times for complex queries by approximately 75%—from 10 seconds to just 2–3 seconds—empowering users to view responses as they’re generated rather than waiting for complete answers.

n this two-part series, we discuss a hub and spoke architecture pattern for building a multi-tenant and multi-account architecture. This pattern supports abstractions for shared services across use cases and teams, helping create secure, scalable, and reliable generative AI systems. In Part 1, we present a centralized hub for generative AI service abstractions and tenant-specific spokes, using AWS Transit Gateway for cross-account interoperability.

Today, we’re excited to announce a significant improvement to the developer experience of Amazon Bedrock: API keys. API keys provide quick access to the Amazon Bedrock APIs, streamlining the authentication process so that developers can focus on building rather than configuration.

In this post, we show how Bayer Crop Science manages large-scale data science operations by training models for their data analytics needs and maintaining high-quality code documentation to support developers. Through these solutions, Bayer Crop Science projects up to a 70% reduction in developer onboarding time and up to a 30% improvement in developer productivity.

In this post, we show how to use Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics to build a financial fraud detection solution.

In this post, we demonstrate how to build an end-to-end solution for text classification using the Amazon Bedrock batch inference capability with the Anthropic’s Claude Haiku model. We walk through classifying travel agency call center conversations into categories, showcasing how to generate synthetic training data, process large volumes of text data, and automate the entire workflow using AWS services.

In this post, we demonstrate how to use the evaluation features of Amazon Bedrock to deliver reliable results across language barriers without the need for localized prompts or custom infrastructure. Through comprehensive testing and analysis, we share practical strategies to help reduce the cost and complexity of multilingual evaluation while maintaining high standards across global large language model (LLM) deployments.

The Cohere Embed 4 multimodal embeddings model is now generally available on Amazon SageMaker JumpStart. The Embed 4 model is built for multimodal business documents, has leading multilingual capabilities, and offers notable improvement over Embed 3 across key benchmarks. In this post, we discuss the benefits and capabilities of this new model. We also walk you through how to deploy and use the Embed 4 model using SageMaker JumpStart.

INRIX pioneered the use of GPS data from connected vehicles for transportation intelligence. In this post, we partnered with Amazon Web Services (AWS) customer INRIX to demonstrate how Amazon Bedrock can be used to determine the best countermeasures for specific city locations using rich transportation data and how such countermeasures can be automatically visualized in street view images. This approach allows for significant planning acceleration compared to traditional approaches using conceptual drawings.