Category: Artificial Intelligence

The collaboration between Clario and AWS demonstrated the potential of AWS AI and machine learning (AI/ML) services and generative AI models, such as Anthropic’s Claude, to streamline document generation processes in the life sciences industry and, specifically, for complicated clinical trial processes.

This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We’ll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.

This post demonstrates how to integrate open-source multi-agent framework, LangGraph, with HAQM Bedrock. It explains how to use LangGraph and HAQM Bedrock to build powerful, interactive multi-agent applications that use graph-based orchestration.

This post demonstrates how enterprises can implement a scalable agentic text-to-SQL solution using HAQM Bedrock Agents, with advanced error-handling tools and automated schema discovery to enhance database query efficiency.

This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate HAQM Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.

The AWS LLM League was designed to lower the barriers to entry in generative AI model customization by providing an experience where participants, regardless of their prior data science experience, could engage in fine-tuning LLMs. Using HAQM SageMaker JumpStart, attendees were guided through the process of customizing LLMs to address real business challenges adaptable to their domain.

In this post, we explore the challenges of large-scale frontier model training, focusing on hardware failures and the benefits of HAQM SageMaker HyperPod – a solution that minimizes disruptions, enhances efficiency, and reduces training costs.

The introduction of HAQM Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with HAQM Nova models as a baseline. We conducted a comprehensive comparison study between model customization and RAG using the latest HAQM Nova models, and share these valuable insights.

In this post, we demonstrate how to use HAQM Personalize and HAQM Bedrock to generate personalized outreach emails for individual users using a video-on-demand use case. This concept can be applied to other domains, such as compelling customer experiences for ecommerce and digital marketing use cases.

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using HAQM Bedrock and CrewAI. We demonstrate how to build a multi-agent system that can automatically summarize new regulations, assess their impact on operations, and provide prescriptive technical guidance. You’ll learn how to use HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.