Category: Amazon Bedrock

This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock, AWS Lambda, and Amazon CloudWatch to create an AI assistant for effective incident management. This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents. The solution uses the power of Amazon 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 Amazon 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 Amazon Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.

In this post, we explore why Asure used the Amazon Web Services (AWS) post-call analytics (PCA) pipeline that generated insights across call centers at scale with the advanced capabilities of generative AI-powered services such as Amazon Bedrock and Amazon Q in QuickSight. Asure chose this approach because it provided in-depth consumer analytics, categorized call transcripts around common themes, and empowered contact center leaders to use natural language to answer queries. This ultimately allowed Asure to provide its customers with improvements in product and customer experiences.

Today, we’re excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on Amazon Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

Today, we’re announcing a significant enhancement to Amazon Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement. This powerful capability enables security and compliance teams to establish mandatory guardrails for every model inference call, making sure organizational safety policies are consistently enforced across AI interactions. This feature enhances AI governance by enabling centralized control over guardrail implementation.

In this post, we explore using Amazon Bedrock to create a text-to-SQL application using RAG. We use Anthropic’s Claude 3.5 Sonnet model to generate SQL queries, Amazon Titan in Amazon Bedrock for text embedding and Amazon Bedrock to access these models.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

Healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. LLMs lack the ability to seamlessly access and synthesize data from these diverse and distributed sources. This limits their potential to provide comprehensive and well-informed insights for healthcare applications. In this blog post, we will explore how Mistral LLM on Amazon Bedrock can address these challenges and enable the development of intelligent healthcare agents with LLM function calling capabilities, while maintaining robust data security and privacy through Amazon Bedrock Guardrails.