Artificial Intelligence
Embed a live AI browser agent in your React app with Amazon Bedrock AgentCore
This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run.
Power Your LLM Training and Evaluation with the New SageMaker AI Generative AI Tools
Today we are excited to introduce the Text Ranking and Question and Answer UI templates to SageMaker AI customers. In this blog post, we’ll walk you through how to set up these templates in SageMaker to create high-quality datasets for training your large language models.
Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker
In this post, we show you how to implement an audio and video segmentation solution using SageMaker Ground Truth. We guide you through deploying the necessary infrastructure using AWS CloudFormation, creating an internal labeling workforce, and setting up your first labeling job. By the end of this post, you will have a fully functional audio/video segmentation workflow that you can adapt for various use cases, from training speech synthesis models to improving video generation capabilities.
Accelerate custom labeling workflows in Amazon SageMaker Ground Truth without using AWS Lambda
Amazon SageMaker Ground Truth enables the creation of high-quality, large-scale training datasets, essential for fine-tuning across a wide range of applications, including large language models (LLMs) and generative AI. By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and time required for data labeling. Whether it’s annotating images, videos, or […]
Use IP-restricted presigned URLs to enhance security in Amazon SageMaker Ground Truth
While presigned URLs offer a convenient way to grant temporary access to S3 objects, sharing these URLs with people outside of the workteam can lead to unintended access of those objects. To mitigate this risk and enhance the security of SageMaker Ground Truth labeling tasks, we have introduced a new feature that adds an additional layer of security by restricting access to the presigned URLs to the worker’s IP address or virtual private cloud (VPC) endpoint from which they access the labeling task. In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial.



