Category: Learning Levels

In this post, we explore how Crop.photo uses HAQM 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 HAQM 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 HAQM 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 HAQM Q Business without a large investment of time at the console, then this solution is for you.

Untold Studios is a tech-driven, leading creative studio specializing in high-end visual effects and animation. This post details how we used HAQM Bedrock to create an AI assistant (Untold Assistant), providing artists with a straightforward way to access our internal resources through a natural language interface integrated directly into their existing Slack workflow.

As technology continues to evolve, newer models are emerging, offering higher quality, increased flexibility, and faster image generation capabilities. One such groundbreaking model is Stable Diffusion XL (SDXL), released by StabilityAI, advancing the text-to-image generative AI technology to unprecedented heights. In this post, we demonstrate how to efficiently fine-tune the SDXL model using SageMaker Studio. We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered HAQM EC2 Inf2 instances, unlocking superior price performance for your inference workloads.

Integrating NeMo Guardrails with Large Language Models (LLMs) is a powerful step forward in deploying AI in customer-facing applications. The example of AnyCompany Pet Supplies illustrates how these technologies can enhance customer interactions while handling refusal and guiding the conversation toward the implemented outcomes. This journey towards ethical AI deployment is crucial for building sustainable, trust-based relationships with customers and shaping a future where technology aligns seamlessly with human values.

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 HAQM 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.

DeepSeek-R1 is an advanced large language model that combines reinforcement learning, chain-of-thought reasoning, and a Mixture of Experts architecture to deliver efficient, interpretable responses while maintaining safety through HAQM Bedrock Guardrails integration.

RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLM’s knowledge, and generates responses with more human-like language. However, in order for generative AI to understand your data, some amount of data preparation is required, which involves a big learning curve. In this post, we walk you through how to convert your existing Aurora data into an index without needing data preparation for HAQM Kendra to perform data search and implement RAG that combines your data along with LLM knowledge to produce accurate responses.

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.