AWS Machine Learning Blog

Category: Generative AI

Understanding prompt engineering: Unlock the creative potential of Stability AI models on AWS

Stability AI’s newest launch of Stable Diffusion 3.5 Large (SD3.5L) on Amazon SageMaker JumpStart enhances image generation, human anatomy rendering, and typography by producing more diverse outputs and adhering closely to user prompts, making it a significant upgrade over its predecessor. In this post, we explore advanced prompt engineering techniques that can enhance the performance of these models and facilitate the creation of compelling imagery through text-to-image transformations.

Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

In this post, we explore the approach behind building an AWS AI-powered Chrome extension that aims to revolutionize the live streaming experience by providing real-time transcription, translation, and summarization capabilities directly within your browser.

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.

Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

This post describes how Agmatix, a pioneering Agtech company powering R&D for input companies and digital agronomic solutions, uses Amazon Bedrock and AWS fully featured services to enhance the research process and development of higher-yielding seeds and sustainable molecules for global agriculture.

Generate financial industry-specific insights using generative AI and in-context fine-tuning

In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. This is done by providing large language models (LLMs) in-context sample data with features and labels in the prompt. The results are similar to fine-tuning LLMs without the complexities of fine-tuning models.

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the pre-built solutions available in SageMaker JumpStart and the customizable Meta Llama 3.2 models, you can unlock the models’ enhanced reasoning, code generation, and instruction-following capabilities to tailor them for your unique use cases.

Discover insights with the Amazon Q Business Microsoft Teams connector

Microsoft Teams is an enterprise collaboration tool that allows you to build a unified workspace for real-time collaboration and communication, meetings, and file and application sharing. You can exchange and store valuable organizational knowledge within Microsoft Teams. Microsoft Teams data is often siloed across different teams, channels, and chats, making it difficult to get a […]

Unleashing Stability AI’s most advanced text-to-image models for media, marketing and advertising: Revolutionizing creative workflows

To stay competitive, media, advertising, and entertainment enterprises need to stay abreast of recent dramatic technological developments. Generative AI has emerged as a game-changer, offering unprecedented opportunities for creative professionals to push boundaries and unlock new realms of possibility. At the forefront of this revolution is Stability AI’s  family of cutting-edge text-to-image AI models. These […]

Build a multi-tenant generative AI environment for your enterprise on AWS

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]