Category: *Post Types

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 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.

Today, we are excited to announce that Qwen3, the latest generation of large language models (LLMs) in the Qwen family, is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI applications on AWS. In this post, we demonstrate how to get started with Qwen3 on Amazon Bedrock Marketplace and SageMaker JumpStart.

In this post, we explore how Swisscom developed their Network Assistant. We discuss the initial challenges and how they implemented a solution that delivers measurable benefits. We examine the technical architecture, discuss key learnings, and look at future enhancements that can further transform network operations.

In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.

In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application.