AWS Machine Learning Blog
Category: Artificial Intelligence
Dive deep into vector data stores using Amazon Bedrock Knowledge Bases
In this post, we dive deep into the vector database options available as part of Amazon Bedrock Knowledge Bases and the applicable use cases, and look at working code examples.
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
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.
Exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision
In this post we discuss how you can maintain access to Lookout for Vision after it is closed to new customers, some alternatives to Lookout for Vision, and how you can export your data from Lookout for Vision to migrate to an alternate solution.
Unlock the knowledge in your Slack workspace with Slack connector for Amazon Q Business
In this post, we will demonstrate how to set up Slack connector for Amazon Q Business to sync communications from both public and private channels, reflective of user permissions.
Transitioning off Amazon Lookout for Metrics
In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.
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.
Automate user on-boarding for financial services with a digital assistant powered by Amazon Bedrock
In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.