AWS Machine Learning Blog
Category: Artificial Intelligence
Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1
In this post, we show you how to create accurate and reliable agents. Agents helps you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps.
AWS recognized as a first-time Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms
AWS has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. The post highlights how AWS’s continued innovations in services like Amazon Bedrock and Amazon SageMaker have enabled organizations to unlock the transformative potential of generative AI.
Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrock
In this post, we presented how to create a fully serverless voice-based contextual chatbot using Amazon Bedrock with Anthropic Claude.
Maintain access and consider alternatives for Amazon Monitron
This post discusses how customers can maintain access to Amazon Monitron after it is closed to new customers and what some alternatives are to Amazon Monitron.
Import a question answering fine-tuned model into Amazon Bedrock as a custom model
In this post, we provide a step-by-step approach of fine-tuning a Mistral model using SageMaker and import it into Amazon Bedrock using the Custom Import Model feature.
Using task-specific models from AI21 Labs on AWS
In this blog post, we will show you how to leverage AI21 Labs’ Task-Specific Models (TSMs) on AWS to enhance your business operations. You will learn the steps to subscribe to AI21 Labs in the AWS Marketplace, set up a domain in Amazon SageMaker, and utilize AI21 TSMs via SageMaker JumpStart.
Architecture to AWS CloudFormation code using Anthropic’s Claude 3 on Amazon Bedrock
In this post, we explore some ways you can use Anthropic’s Claude 3 Sonnet’s vision capabilities to accelerate the process of moving from architecture to the prototype stage of a solution.
How Northpower used computer vision with AWS to automate safety inspection risk assessments
In this post, we share how Northpower has worked with their technology partner Sculpt to reduce the effort and carbon required to identify and remediate public safety risks. Specifically, we cover the computer vision and artificial intelligence (AI) techniques used to combine datasets into a list of prioritized tasks for field teams to investigate and mitigate.
GenAI for Aerospace: Empowering the workforce with expert knowledge on Amazon Q and Amazon Bedrock
In this post we show how you can quickly launch generative AI-enabled expert chatbots, trained on your proprietary document sets, to empower your workforce across specific aerospace roles with Amazon Q and Amazon Bedrock.
Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study
In this post, we share an ML infrastructure architecture that uses SageMaker HyperPod to support research team innovation in video generation. We will discuss the advantages and pain points addressed by SageMaker HyperPod, provide a step-by-step setup guide, and demonstrate how to run a video generation algorithm on the cluster.