Amazon Titan Text Express and Amazon Titan Text Lite are large language models (LLMs) that help customers improve productivity and efficiency for an extensive range of text-related tasks, and offer price and performance options that are optimized for your needs. You can now access these Amazon Titan Text foundation models in Amazon Bedrock, which helps you easily build and scale generative AI applications with new text generation capabilities.

Anthropic’s Claude 2.1 foundation model is now generally available in Amazon Bedrock. Claude 2.1 delivers key capabilities for enterprises, such as an industry-leading 200,000 token context window (2x the context of Claude 2.0), reduced rates of hallucination, improved accuracy over long documents, system prompts, and a beta tool use feature for function calling and workflow orchestration. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies, like Anthropic, along with a broad set of capabilities that provide you with the easiest way to build and scale generative AI applications with foundation models.

Today, AWS announces the general availability of Amazon SageMaker HyperPod, which reduces time to train foundation models (FMs) by up to 40% by providing purpose-built infrastructure for distributed training at scale. 

Today, AWS announces the general availability of vector engine for Amazon OpenSearch Serverless. Vector engine for OpenSearch Serverless is a simple, scalable, and high-performing vector database which makes it easier for developers to build machine learning (ML)–augmented search experiences and generative artificial intelligence (AI) applications without having to manage the underlying vector database infrastructure. Developers can rely on the vector engine’s cost-efficient, secure, and mature serverless platform to seamlessly transition from application prototyping to production. 

AWS Clean Rooms ML (Preview) helps you and your partners apply privacy-enhancing ML to generate predictive insights without having to share raw data with each other. The capability’s first model is specialized to help companies create lookalike segments. With AWS Clean Rooms ML lookalike modeling, you can train your own custom model using your data, and invite your partners to bring a small sample of their records to a collaboration to generate an expanded set of similar records while protecting you and your partner’s underlying data. Healthcare modeling will be available in the coming months.

Amazon OpenSearch Service zero-ETL integration with Amazon S3, a new way for customers to query operational logs in Amazon S3 and S3-based data lakes without needing to switch between tools to analyze operational data, is available for customer preview. Customers can boost the performance of their queries and build fast-loading dashboards using the built-in query acceleration capabilities of Amazon OpenSearch Service zero-ETL integration with Amazon S3.

Today, AWS announces the preview release of AWS Clean Rooms Differential Privacy, a new capability that helps you protect the privacy of your users with mathematically-backed and intuitive controls in a few clicks. As a fully managed capability, no prior differential privacy experience is needed to help you prevent the re-identification of your users. 

Amazon MemoryDB for Redis now supports vector search in preview, a new capability that enables you to store, index, and search vectors. MemoryDB is a database that combines in-memory performance with multi-AZ durability. With vector search for MemoryDB, you can develop real-time machine learning (ML) and generative AI applications with the highest performance demands using the popular, open-source Redis API. Vector search for MemoryDB supports storing millions of vectors, with single-digit millisecond query and update response times, and tens of thousands queries per second (QPS) at greater than 99% recall. You can generate vector embeddings using AI/ML services like Amazon Bedrock and SageMaker, and store them within MemoryDB.

Amazon DocumentDB (with MongoDB compatibility) now supports vector search, a new capability that enables you to store, index, and search millions of vectors with millisecond response times. Vectors are numerical representations of unstructured data, such as text, created from machine learning (ML) models that help capture the semantic meaning of the underlying data. Vector search for Amazon DocumentDB can store vectors from Amazon Bedrock, Amazon SageMaker, and more. There are no upfront commitments or additional costs to use vector search, and you only pay for the data you store and compute resources you use. 

Amazon Redshift introduces Amazon Q generative SQL in Amazon Redshift Query Editor, an out-of-the-box web-based SQL editor for Redshift, to simplify query authoring and increase your productivity by allowing you to express queries in natural language and receive SQL code recommendations. Furthermore, it allows you to get insights faster without extensive knowledge of your organization’s complex database metadata.