AWS Machine Learning Blog

Category: Learning Levels

Appian Architecture diagram

Revolutionizing business processes with Amazon Bedrock and Appian’s generative AI skills

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.

Solution Architecture

Accelerate your Amazon Q implementation: starter kits for SMBs

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.

Architecture Diagram

How Untold Studios empowers artists with an AI assistant built on Amazon Bedrock

Untold Studios is a tech-driven, leading creative studio specializing in high-end visual effects and animation. This post details how we used Amazon 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.

This digram show cases the value prop of using LoRA fine tuning techniques

Fine-tune and host SDXL models cost-effectively with AWS Inferentia2

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 Amazon EC2 Inf2 instances, unlocking superior price performance for your inference workloads.

The flow from input forms to the final output, including how integrations and AI services are utilized.

Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

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.

Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design

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.

DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

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 Amazon Bedrock Guardrails integration.

ML-16454_solution_architecture.jpg

Develop a RAG-based application using Amazon Aurora with Amazon Kendra

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 Amazon Kendra to perform data search and implement RAG that combines your data along with LLM knowledge to produce accurate responses.

Create a SageMaker inference endpoint with custom model & extended container

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.

Security best practices to consider while fine-tuning models in Amazon Bedrock

In this post, we implemented secure fine-tuning jobs in Amazon Bedrock, which is crucial for protecting sensitive data and maintaining the integrity of your AI models. By following the best practices outlined in this post, including proper IAM role configuration, encryption at rest and in transit, and network isolation, you can significantly enhance the security posture of your fine-tuning processes.