Category: Artificial Intelligence

In this post, we show you how to unlock powerful post-call analytics and visualizations, empowering your organization to make data-driven decisions and drive continuous improvement.

In this post, we demonstrate how to deploy a contextual AI assistant. We build a solution which provides users with a familiar and convenient interface using Amazon Bedrock Knowledge Bases, Amazon Lex, and Amazon Connect, with WhatsApp as the channel.

In this post, we explore how organizations can cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. We discuss how these powerful tools enable organizations to optimize compute resources and reduce the complexity of model training and fine-tuning. We explore how you can make an informed decision about which Amazon SageMaker service is most applicable to your business needs and requirements.

In this post, we will show you how to set up an automated LLM customization (fine-tuning) workflow so that the Llama 3.x models from Meta can provide a high-quality summary of SEC filings for financial applications. Fine-tuning allows you to configure LLMs to achieve improved performance on your domain-specific tasks.

In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.

We’re pleased to announce the general availability (GA) of Amazon Bedrock Custom Model Import. This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API.

In this post, we explore a sample solution that you can use to deploy an image editing application by using AWS serverless services and generative AI services. We use Amazon Bedrock and an Amazon Titan FM that allow you to edit images by using prompts.

In this post, we will introduce how to use an Amazon EC2 Inf2 instance to cost-effectively deploy multiple industry-leading LLMs on AWS Inferentia2, a purpose-built AWS AI chip, helping customers to quickly test and open up an API interface to facilitate performance benchmarking and downstream application calls at the same time.

In this post, we dive into the architectural considerations and development lifecycle practices that can help you build robust, scalable, and secure intelligent agents.

In this post we introduce an innovative solution for end-to-end model customization and deployment at the edge using Amazon SageMaker and Qualcomm AI Hub.