AWS Machine Learning Blog

Amazon Bedrock Data Automation in public preview, offers a unified experience for developers of all skillsets to easily automate the extraction, transformation, and generation of relevant insights from documents, images, audio, and videos to build generative AI–powered applications. In this post, we demonstrate how to use Amazon Bedrock Data Automation in the AWS Management Console and the AWS SDK for Python (Boto3) for media analysis and intelligent document processing (IDP) workflows.

TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.

Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI, using advanced SageMaker AI features for optimal performance and cost management.

We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressions—and to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications.

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using […]

Ensemble models are becoming popular within the ML communities. They generate more accurate predictions through combining the predictions of multiple models. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models. This enables developers to build highly accurate models while maintaining efficiency, and reproducibility. In this post, we provide an example of an ensemble model that was trained and deployed using Pipelines.

In this post, we explore a solution for implementing load balancing across login nodes in Slurm-based HyperPod clusters. By distributing user activity evenly across all available nodes, this approach provides more consistent performance, better resource utilization, and a smoother experience for all users. We guide you through the setup process, providing practical steps to achieve effective load balancing in your HyperPod clusters.

In this post, we explore Clearwater Analytics’ foray into generative AI, how they’ve architected their solution with Amazon SageMaker, and dive deep into how Clearwater Analytics is using LLMs to take advantage of more than 18 years of experience within the investment management domain while optimizing model cost and performance.

In this post, we demonstrate how we innovated to build a Retrieval Augmented Generation (RAG) application with agentic workflow and a knowledge base on Amazon Bedrock. We implemented the RAG pipeline in a Slack chat-based assistant to empower the Amazon Twitch ads sales team to move quickly on new sales opportunities.

Bedrock multi-agent collaboration enables developers to build, deploy, and manage multiple specialized agents working together seamlessly to address increasingly complex business workflows. In this post, we show you how agentic workflows with Amazon Bedrock Agents can help accelerate this journey for research scientists with a natural language interface. We define an example analysis pipeline, specifically for lung cancer survival with clinical, genomics, and imaging modalities of biomarkers. We showcase a variety of specialized agents including a biomarker database analyst, statistician, clinical evidence researcher, and medical imaging expert in collaboration with a supervisor agent. We demonstrate advanced capabilities of agents for self-review and planning that help build trust with end users by breaking down complex tasks into a series of steps and showing the chain of thought to generate the final answer.