Category: Intermediate (200)

In this post, we will harness the power of generative artificial intelligence (AI) and Amazon Bedrock to help organizations simplify and effectively manage remediations of AWS Security Hub control findings.

Through this series of posts, we share our generative AI journey and use cases, detailing the architecture, AWS services used, lessons learned, and the impact of these solutions on our teams and customers. In this first post, we explore Account Summaries, one of our initial production use cases built on Amazon Bedrock. Account Summaries equips our teams to be better prepared for customer engagements. It combines information from various sources into comprehensive, on-demand summaries available in our CRM or proactively delivered based on upcoming meetings. From the period of September 2023 to March 2024, sellers leveraging GenAI Account Summaries saw a 4.9% increase in value of opportunities created.

Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems, which each user is authorized to access. In an earlier post, we discussed how you can build private and secure enterprise generative AI applications with Amazon Q Business and AWS IAM Identity Center. If you want to use Amazon Q Business to build enterprise generative AI applications, and have yet to adopt organization-wide use of AWS IAM Identity Center, you can use Amazon Q Business IAM Federation to directly manage user access to Amazon Q Business applications from your enterprise identity provider (IdP), such as Okta or Ping Identity. Amazon Q Business IAM Federation uses Federation with IAM and doesn’t require the use of IAM Identity Center. This post shows how you can use Amazon Q Business IAM Federation for user access management of your Amazon Q Business applications.

In this post, we provide an overview of Amazon Q Business Confluence Cloud connector and how you can use it for seamless integration of generative AI assistance to your Confluence Cloud.

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators.

Today, we are excited to announce general availability of batch inference for Amazon Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.

Fine-tuning Meta Llama 3.1 models with Amazon SageMaker JumpStart enables developers to customize these publicly available foundation models (FMs). The Meta Llama 3.1 collection represents a significant advancement in the field of generative artificial intelligence (AI), offering a range of capabilities to create innovative applications. The Meta Llama 3.1 models come in various sizes, with 8 billion, 70 billion, and 405 billion parameters, catering to diverse project needs. In this post, we demonstrate how to fine-tune Meta Llama 3-1 pre-trained text generation models using SageMaker JumpStart.

This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]

This post demonstrates how you can bring your existing SageMaker Data Wrangler flows—the instructions created when building data transformations—from SageMaker Studio Classic to SageMaker Canvas. We provide an example of moving files from SageMaker Studio Classic to Amazon Simple Storage Service (Amazon S3) as an intermediate step before importing them into SageMaker Canvas.