Category: Technical How-to

Stable Diffusion XL by Stability AI is a high-quality text-to-image deep learning model that allows you to generate professional-looking images in various styles. Managed versions of Stable Diffusion XL are already available to you on Amazon SageMaker JumpStart (see Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio) and Amazon Bedrock (see […]

This post shows you how to apply AWS services such as Amazon Bedrock, AWS Step Functions, and Amazon Simple Email Service (Amazon SES) to build a fully-automated multilingual calendar artificial intelligence (AI) assistant. It understands the incoming messages, translates them to the preferred language, and automatically sets up calendar reminders.

Amazon Bedrock Knowledge Bases is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). This feature streamlines the entire RAG workflow, from ingestion to retrieval and prompt augmentation, eliminating the need for custom data source integrations and data flow management. […]

You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you don’t always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive […]

Originally PyTorch used an eager mode where each PyTorch operation that forms the model is run independently as soon as it’s reached. PyTorch 2.0 introduced torch.compile to speed up PyTorch code over the default eager mode. In contrast to eager mode, the torch.compile pre-compiles the entire model into a single graph in a manner that’s optimal for […]

In November 2023, we announced Amazon Bedrock Knowledge Bases as generally available. Knowledge bases allow Amazon Bedrock users to unlock the full potential of Retrieval Augmented Generation (RAG) by seamlessly integrating their company data into the language model’s generation process. This feature allows organizations to harness the power of large language models (LLMs) while making […]

Organizations strive to implement efficient, scalable, cost-effective, and automated customer support solutions without compromising the customer experience. Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledge base without the involvement of live agents. These chatbots can be efficiently utilized for handling generic inquiries, freeing up […]

In this post, we show how you can automate and intelligently process derivative confirms at scale using AWS AI services. The solution combines Amazon Textract, a fully managed ML service to effortlessly extract text, handwriting, and data from scanned documents, and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications, all without managing servers.

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Financial analysts and research analysts in capital markets distill business insights from financial and non-financial data, […]

In this post, we explore how to integrate LLMs into enterprise applications to harness their generative capabilities. We delve into the technical aspects of workflow implementation and provide code samples that you can quickly deploy or modify to suit your specific requirements. Whether you’re a developer seeking to incorporate LLMs into your existing systems or a business owner looking to take advantage of the power of NLP, this post can serve as a quick jumpstart.