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New Tools for Building with Generative AI on AWS

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Generative AI, which involves AI systems creating new content and ideas such as conversations, stories, images, videos, and music, has gained significant attention in recent times. Amazon Web Services (AWS) aims to democratize access to generative AI and make it easier for customers to integrate it into their businesses. To support the infrastructure needs of generative AI, AWS has announced the general availability of Amazon EC2 Trn1n instances powered by AWS Trainium and Amazon EC2 Inf2 instances powered by AWS Inferentia2. These instances are specifically optimized for machine learning (ML) training and inference, offering high performance and cost-effectiveness.

AWS has been focusing on AI and ML for over 20 years, with many of its capabilities driven by ML. From e-commerce recommendations to robotic picking routes optimization in fulfillment centers, supply chain management, forecasting, and capacity planning, ML plays a crucial role. Technologies like Prime Air and computer vision in Amazon Go also rely on ML. With over 100,000 customers of all sizes and industries, AWS has played a key role in democratizing ML and making it accessible. It offers a wide range of AI and ML services, including infrastructure for ML training and inference, Amazon SageMaker for building and deploying models, and various services for adding AI capabilities to applications.

Generative AI is powered by ML models known as Foundation Models (FMs), which are pre-trained on vast amounts of data. Recent advancements in ML have led to the development of FMs with billions of parameters, enabling them to perform a wide range of tasks across different domains. FMs are general-purpose models that can be customized for specific functions using a small fraction of the data and compute required to train a model from scratch. This customization allows companies to create unique customer experiences and tailor the models to their specific needs.

To address the challenges faced by customers in accessing and integrating FMs, AWS has introduced Amazon Bedrock. Bedrock provides access to a range of powerful FMs from AI21 Labs, Anthropic, Stability AI, and Amazon via an API. It offers a serverless experience, allowing customers to find the right model, customize it with their own data, and easily integrate and deploy it into their applications using familiar AWS tools. Bedrock ensures data privacy and security by encrypting all data and keeping it within the customer’s Virtual Private Cloud (VPC).

Additionally, AWS has introduced the Titan FMs, which consist of two new large language models (LLMs). The first Titan model is a generative LLM suitable for tasks like summarization, text generation, classification, and open-ended Q&A. The second model is an embeddings LLM that translates text inputs into numerical representations for applications like personalization and search. The Titan FMs are designed to detect and remove harmful content and provide contextual responses.

AWS also offers specialized infrastructure for generative AI. The Amazon EC2 Trn1n instances powered by AWS Trainium provide significant cost savings for ML training, while the Amazon EC2 Inf2 instances powered by AWS Inferentia deliver high-performance inference with low latency and high throughput networking.

By introducing Amazon Bedrock, Titan FMs, and specialized infrastructure, AWS aims to make generative AI accessible to companies of all sizes, accelerating the use of ML across organizations. The availability of FMs through a managed service and the ease of customization enable developers to build their own generative AI applications quickly and securely. AWS continues to drive innovation in ML and AI, empowering customers to transform their businesses with these technologies.

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