New Tools for Building with Generative AI on AWS

13 Apr 23

Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by ML models—very large models that are pre-trained on vast amounts of data and commonly referred to as Foundation Models (FMs). Recent advancements in ML (specifically the invention of the transformer-based neural network architecture) have led to the rise of models that contain billions of parameters or variables. To give a sense for the change in scale, the largest pre-trained model in 2019 was 330M parameters. Now, the largest models are more than 500B parameters—a 1,600x increase in size in just a few years. Today’s FMs, such as the large language models (LLMs) GPT3.5 or BLOOM, and the text-to-image model Stable Diffusion from Stability AI, can perform a wide range of tasks that span multiple domains, like writing blog posts, generating images, solving math problems, engaging in dialog, and answering questions based on a document. The size and general-purpose nature of FMs make them different from traditional ML models, which typically perform specific tasks, like analyzing text for sentiment, classifying images, and forecasting trends.

Amazon Bedrock, a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. Bedrock is the easiest way for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders. Bedrock will offer the ability to access a range of powerful FMs for text and images—including Amazon’s Titan FMs, which consist of two new LLMs through a scalable, reliable, and secure AWS managed service. With Bedrock’s serverless experience, customers can easily find the right model for what they’re trying to get done, get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS tools and capabilities they are familiar with, without having to manage any infrastructure (including integrations with Amazon SageMaker ML features like Experiments to test different models and Pipelines to manage their FMs at scale).