AWS Machine Learning Blog

Category: Artificial Intelligence

Improve LLM application robustness with Amazon Bedrock Guardrails and Amazon Bedrock Agents

In this post, we demonstrate how Amazon Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with Amazon Bedrock Agents.

Enable or disable ACL crawling safely in Amazon Q Business

Amazon Q Business recently added support for administrators to modify the default access control list (ACL) crawling feature for data source connectors. Amazon Q Business is a fully managed, AI powered assistant with enterprise-grade security and privacy features. It includes over 40 data source connectors that crawl and index documents. By default, Amazon Q Business […]

SK Telecom improves telco-specific Q&A by fine-tuning Anthropic’s Claude models in Amazon Bedrock

SK Telecom improves telco-specific Q&A by fine-tuning Anthropic’s Claude models in Amazon Bedrock

In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock.

Scaling Rufus, the Amazon generative AI-powered conversational shopping assistant with over 80,000 AWS Inferentia and AWS Trainium chips, for Prime Day

In this post, we dive into the Rufus inference deployment using AWS chips and how this enabled one of the most demanding events of the year—Amazon Prime Day.

Efficient Pre-training of Llama 3-like model architectures using torchtitan on Amazon SageMaker

Efficient Pre-training of Llama 3-like model architectures using torchtitan on Amazon SageMaker

In this post, we collaborate with the team working on PyTorch at Meta to showcase how the torchtitan library accelerates and simplifies the pre-training of Meta Llama 3-like model architectures. We showcase the key features and capabilities of torchtitan such as FSDP2, torch.compile integration, and FP8 support that optimize the training efficiency.

Time series forecasting with Amazon SageMaker AutoML

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment.