Category: HAQM Bedrock

HAQM Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. In this post, we discuss how to get started with image content filters in HAQM Bedrock Guardrails.

In this post, we explore how you can use HAQM Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test data creation for multiclass classification supervised learning use cases. We dive deep into this process on how to use XML tags to structure the prompt and guide HAQM Bedrock in generating a balanced label dataset with high accuracy. We also showcase a real-world example for predicting the root cause category for support cases. This use case, solvable through ML, can enable support teams to better understand customer needs and optimize response strategies.

In this post, we explore how to modify your Regional access controls to specifically allow HAQM Bedrock cross-Region inference while maintaining broader Regional restrictions for other AWS services. We provide practical examples for both SCP modifications and AWS Control Tower implementations.

Generative AI has emerged as a game changer, offering unprecedented opportunities for game designers to push boundaries and create immersive virtual worlds. At the forefront of this revolution is Stability AI’s cutting-edge text-to-image AI model, Stable Diffusion 3.5 Large (SD3.5 Large), which is transforming the way we approach game environment creation. In this post, we explore how you can use SD3.5 Large to address practical gaming needs such as early concept art and character design.

HAQM Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across […]

In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.

In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. By extracting and structuring the key information from the source materials, the models can create a searchable knowledge base that allows you to quickly locate the data, formulas, and visualizations you need to support your work.

This post presents a comprehensive AIOps solution that combines various AWS services such as HAQM Bedrock, AWS Lambda, and HAQM CloudWatch to create an AI assistant for effective incident management. This solution also uses HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents. The solution uses the power of HAQM Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.

AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of HAQM Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.

In this post, we demonstrate how to use HAQM Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.