Category: Technical How-to

In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.

In this post, we will introduce how to use an Amazon EC2 Inf2 instance to cost-effectively deploy multiple industry-leading LLMs on AWS Inferentia2, a purpose-built AWS AI chip, helping customers to quickly test and open up an API interface to facilitate performance benchmarking and downstream application calls at the same time.

In this post, we discuss how you can use AWS HealthScribe with Amazon Q Business to create a chatbot to quickly gain insights into patient clinician conversations.

In this post, we explore three scenarios demonstrating the versatility of integrating Amazon EFS with SageMaker Studio. These scenarios highlight how Amazon EFS can provide a scalable, secure, and collaborative data storage solution for data science teams.

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. This example not only highlights the efficiency of SageMaker, but also its impact how geospatial ML can be used to monitor the environment for sustainability and conservation purposes.

In this post, we present an Amazon Bedrock powered virtual assistant that can transcribe presentation audio and examine it for language use, grammatical errors, filler words, and repetition of words and sentences to provide recommendations as well as suggest a curated version of the speech to elevate the presentation.

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.

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.

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.

In this post, we discuss how you can use the ApplyGuardrail API in common generative AI architectures such as third-party or self-hosted large language models (LLMs), or in a self-managed Retrieval Augmented Generation (RAG) architecture.