Amazon Web Services

Generative AI applications should be developed with adequate controls for steering the behavior of FMs. Responsible AI considerations such as privacy, security, safety, controllability, fairness, explainability, transparency and governance help ensure that AI systems are trustworthy. In this post, we demonstrate how to use the AWS generative AI best practices framework on AWS Audit Manager to evaluate this insurance claim agent from a responsible AI lens.

In this blog post, we explore a client services agent assistant application developed by the London Stock Exchange Group (LSEG) using Amazon Q Business. We will discuss how Amazon Q Business saved time in generating answers, including summarizing documents, retrieving answers to complex Member enquiries, and combining information from different data sources (while providing in-text citations to the data sources used for each answer).

This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in Amazon Bedrock. It can help collect more data on the value of LLMs for your content translation use cases.

In this post, we show you how Parameta used Amazon Bedrock Flows to transform their manual client email processing into an automated, intelligent workflow that reduced resolution times from weeks to days while maintaining high accuracy and operational control.

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it.

Optimizing costs of generative AI applications on AWS is critical for realizing the full potential of this transformative technology. The post outlines key cost optimization pillars, including model selection and customization, token usage, inference pricing plans, and vector database considerations.

In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. We use HuggingFace’s Optimum-Neuron software development kit (SDK) to apply LoRA to fine-tuning jobs, and use SageMaker HyperPod as the primary compute cluster to perform distributed training on Trainium. Using LoRA supervised fine-tuning for Meta Llama 3 models, you can further reduce your cost to fine tune models by up to 50% and reduce the training time by 70%.

When building voice-enabled chatbots with Amazon Lex, one of the biggest challenges is accurately capturing user speech input for slot values. Transcription confidence scores can help ensure reliable slot filling. This blog post outlines strategies like progressive confirmation, adaptive re-prompting, and branching logic to create more robust slot filling experiences.

Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

Amazon Q embedded is a feature that lets you embed a hosted Amazon Q Business assistant on your website or application to create more personalized experiences that boost end-users’ productivity. In this post, we demonstrate how to use the Amazon Q embedded feature to add an Amazon Q Business assistant to your website or web application using basic HTML or React.

In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.

This post provides step-by-step instructions for creating a collaborative multi-agent framework with reasoning capabilities to decouple business applications from FMs. It demonstrates how to combine Amazon Bedrock Agents with open source multi-agent frameworks, enabling collaborations and reasoning among agents to dynamically execute various tasks. The exercise will guide you through the process of building a reasoning orchestration system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and FMs. We also explore the integration of Amazon Bedrock Agents with open source orchestration frameworks LangGraph and CrewAI for dispatching and reasoning.

Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. In this post, we explore how Fastweb used cutting-edge AI and ML services to embark on their LLM journey, overcoming challenges and unlocking new opportunities along the way.

In this post, we’ll demonstrate how to configure an Amazon Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided by Amazon Q Business to query real-time data and take actions in ServiceNow.

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.