AWS Machine Learning Blog

Category: Generative AI

Build protein folding workflows to accelerate drug discovery on Amazon SageMaker

Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 billion to go through disease target identification, drug screening, drug-target validation, and eventual commercial launch. […]

Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio

Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image generation model from Stability AI. SDXL 1.0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. It’s designed for professional use, and calibrated for high-resolution […]

AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy

Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what […]

Use generative AI foundation models in VPC mode with no internet connectivity using Amazon SageMaker JumpStart

With recent advancements in generative AI, there are lot of discussions happening on how to use generative AI across different industries to solve specific business problems. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It is all backed by very large models […]

How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. You’re likely familiar with the autocomplete suggestion feature when you search for something on Google or Amazon. Although the search terms in these scenarios are pretty common keywords or expressions that we use in daily life, […]

Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

Amazon SageMaker is an end-to-end machine learning (ML) platform with wide-ranging features to ingest, transform, and measure bias in data, and train, deploy, and manage models in production with best-in-class compute and services such as Amazon SageMaker Data Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Model Registry, Amazon SageMaker Feature Store, Amazon SageMaker […]

Interactively fine-tune Falcon-40B and other LLMs on Amazon SageMaker Studio notebooks using QLoRA

Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models. We utilize Hugging Face’s parameter-efficient fine-tuning (PEFT) library and quantization techniques through bitsandbytes to support interactive fine-tuning of […]

Safe image generation and diffusion models with Amazon AI content moderation services

Generative AI technology is improving rapidly, and it’s now possible to generate text and images based on text input. Stable Diffusion is a text-to-image model that empowers you to create photorealistic applications. You can easily generate images from text using Stable Diffusion models through Amazon SageMaker JumpStart. The following are examples of input texts and […]

Use proprietary foundation models from Amazon SageMaker JumpStart in Amazon SageMaker Studio

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications. SageMaker JumpStart allows you to deploy foundation models from a network isolated environment, and […]

Deploy a serverless ML inference endpoint of large language models using FastAPI, AWS Lambda, and AWS CDK

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in other applications. It can be cumbersome to manage the process, but with the right tool, […]