Category: Artificial Intelligence

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.

In this post, I will share some of these customers’ remarkable journeys, offering practical insights for any organization looking to harness the power of generative AI.

In this blog post, we explore how to integrate NeMo 2.0 with SageMaker HyperPod to enable efficient training of large language models (LLMs). We cover the setup process and provide a step-by-step guide to running a NeMo job on a SageMaker HyperPod cluster.

Today, we are excited to announce that the NeMo Retriever Llama3.2 Text Embedding and Reranking NVIDIA NIM microservices are available in Amazon SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with these models on SageMaker JumpStart.

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.