Category: Intermediate (200)

In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM. Additionally, we provide concrete evaluation results that showcase the power of synthetic data in fine-tuning when data is scarce.

This blog post explores LLM-as-a-judge on Amazon Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.

In this post, we present a streamlined approach to deploying an AI-powered agent by combining Amazon Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.

GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. In this post, we demonstrate how GraphBolt enhances GraphStorm’s performance in distributed settings. We provide a hands-on example of using GraphStorm with GraphBolt on SageMaker for distributed training. Lastly, we share how to use Amazon SageMaker Pipelines with GraphStorm.

In this post, we explore how Crop.photo uses Amazon Rekognition to provide sophisticated image analysis, enabling automated and precise editing of large volumes of images. This integration streamlines the image editing process for clients, providing speed and accuracy, which is crucial in the fast-paced environments of ecommerce and sports.

AWS and Appian’s collaboration marks a significant advancement in business process automation. By using the power of Amazon Bedrock and Anthropic’s Claude models, Appian empowers enterprises to optimize and automate processes for greater efficiency and effectiveness. This blog post will cover how Appian AI skills build automation into organizations’ mission-critical processes to improve operational excellence, reduce costs, and build scalable solutions.

Starter kits are complete, deployable solutions that address common, repeatable business problems. They deploy the services that make up a solution according to best practices, helping you optimize costs and become familiar with these kinds of architectural patterns without a large investment in training. In this post, we showcase a starter kit for Amazon Q Business. If you have a repository of documents that you need to turn into a knowledge base quickly, or simply want to test out the capabilities of Amazon Q Business without a large investment of time at the console, then this solution is for you.

The solution presented in this post demonstrates a powerful pattern for accelerating video and audio review workflows while maintaining human oversight. By combining the power of AI models in Amazon Bedrock with human expertise, you can create tools that not only boost productivity but also maintain the critical element of human judgment in important decision-making processes.

This post walks you through the end-to-end process of deploying a single custom model on SageMaker using NASA’s Prithvi model. The Prithvi model is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. It can be finetuned for image segmentation using the mmsegmentation library for use cases like burn scars detection, flood mapping, and multi-temporal crop classification.

In this post, we show you how to set up Amazon Lex for an omnichannel chatbot experience and Amazon Bedrock to be your secondary validation layer. This allows your customers to potentially provide out-of-band responses both at the intent and slot collection levels without having to be re-prompted, allowing for a seamless customer experience.