Category: Learning Levels

To better protect themselves, many cyclists are starting to ride with cameras mounted to the front or back of their bicycle. In this blog post, I will demonstrate a machine learning solution that cyclists can use to better identify close calls. The architecture of the solution uses Amazon Rekognition to detect vehicles in recorded bike ride videos. It then analyzes the video to determine if any vehicles are passing too close to the cyclist, within the 3-foot safe distance required by law. The solution automatically generates video clips of these dangerous passing events, which can then be shared with authorities to help improve cyclist safety.

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.

Researchers developed Medusa, a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously. This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test. Medusa-1 achieves an inference speedup of around two times without sacrificing model quality, with the exact improvement varying based on model size and data used. In this post, we demonstrate its effectiveness with a 1.8 times speedup observed on a sample dataset.

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.

In this post, we demonstrate how to build a knowledge base solution by integrating enterprise data with Amazon Q Business using Amazon S3. This approach helps organizations improve operational efficiency, reduce response times, and gain valuable insights from their historical data. The solution uses AWS security best practices to promote data protection while enabling teams to create a comprehensive knowledge base from various data sources.

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.