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.

Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best foundation models for your use case. Amazon Bedrock offers a choice of automatic evaluation and human evaluation. You can use automatic evaluation with predefined metrics such as accuracy, robustness, and toxicity. For subjective or custom metrics, such as friendliness, style, and alignment to brand voice, you can set up a human evaluation workflow with a few clicks. Human evaluation workflows can leverage your own employees or an AWS-managed team as reviewers. Model evaluation provides built-in curated datasets or you can bring your own datasets. 

Today, Amazon SageMaker Clarify announces a new capability to support foundation model (FM) evaluations. AWS customers can compare, and select FMs based on metrics such as accuracy, robustness, bias, and toxicity, in minutes.

Today, AWS announces the general availability of Amazon Neptune Analytics, a new analytics database engine. Neptune Analytics makes it faster for data scientists and application developers to get insights and find trends by analyzing graph data with tens of billions of connections in seconds. Neptune Analytics adds to existing Neptune tools and services such as Amazon Neptune Database, Amazon Neptune ML, and visualization tools. Neptune is a fast, reliable, and fully managed graph database service for building and running applications with highly connected datasets, such as knowledge graphs, fraud graphs, identity graphs, and security graphs. With Neptune Analytics, you can find insights in graph data up to 80x faster by analyzing your existing Neptune graph database or graph data from a data lake such as Amazon S3.

Today, Amazon Redshift announces the general availability of support for Apache Iceberg tables. Now, you can easily access your Apache Iceberg tables on your data lake and join it with the data in your data warehouse. This capability offers increased performance whether you are accessing your data lake tables using auto-mounted AWS Glue catalog or external schemas. 

Today, we’re excited to announce the preview of a new smart sifting capability of Amazon SageMaker that automatically inspects and evaluates training data on-the-fly to selectively learn from only the most informative data samples, reducing model training time and cost by up to 35%. You can get started with smart data sifting in minutes without making changes to your existing data pipelines or training scripts.

Amazon OpenSearch Service introduces OR1, the OpenSearch Optimized Instance family, that delivers up to 30% price-performance improvement over existing instances in internal benchmarks and uses Amazon S3 to provide 11 9s of durability. The new OR1 instances are best suited for indexing-heavy workloads, and offers better indexing performance compared to the existing memory optimized instances available on OpenSearch Service.