Today, we’re introducing AWS Clean Rooms ML (preview), a new capability of AWS Clean Rooms that helps you and your partners apply machine learning (ML) models on your collective data without copying or sharing raw data with each other. With this new capability, you can generate predictive insights using ML models while continuing to protect your sensitive data. During this preview, AWS Clean Rooms ML introduces its first model specialized to help companies create lookalike segments for marketing use cases. With AWS Clean Rooms ML lookalike, you can train your own custom model, and you can invite partners to bring a small sample of their records to collaborate and generate an expanded set of similar records while protecting everyone’s underlying data. In…
Tag: AWS News Blog
Announcing Amazon OpenSearch Service zero-ETL integration with Amazon S3 (preview)
Today we are announcing a preview of Amazon OpenSearch Service zero-ETL integration with Amazon S3, a new way to query operational logs in Amazon S3 and S3-based data lakes without needing to switch between services. You can now analyze infrequently queried data in cloud object stores and simultaneously use the operational analytics and visualization capabilities of OpenSearch Service. Amazon OpenSearch Service direct queries with Amazon S3 provides a zero-ETL integration to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling customers to directly query their operational data, reducing costs and time to action. This zero-ETL integration will be configurable within OpenSearch Service, where you can take advantage of various log type templates, including predefined dashboards,…
Analyze large amounts of graph data to get insights and find trends with Amazon Neptune Analytics
I am happy to announce the general availability of Amazon Neptune Analytics, a new analytics database engine that makes it faster for data scientists and application developers to quickly analyze large amounts of graph data. With Neptune Analytics, you can now quickly load your dataset from Amazon Neptune or your data lake on Amazon Simple Storage Service (Amazon S3), run your analysis tasks in near real time, and optionally terminate your graph afterward. Graph data enables the representation and analysis of intricate relationships and connections within diverse data domains. Common applications include social networks, where it aids in identifying communities, recommending connections, and analyzing information diffusion. In supply chain management, graphs facilitate efficient route optimization and bottleneck identification. In cybersecurity,…
Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available
Today, we are announcing the general availability of vector search for Amazon DocumentDB (with MongoDB compatibility), a new built-in capability that lets you store, index, and search millions of vectors with millisecond response times within your document database. Vector search is an emerging technique used in machine learning (ML) to find similar data points to given data by comparing their vector representations using distance or similarity metrics. Vectors are numerical representation of unstructured data created from large language models (LLM) hosted in Amazon Bedrock, Amazon SageMaker, and other open source or proprietary ML services. This approach is useful in creating generative artificial intelligence (AI) applications, such as intuitive search, product recommendation, personalization, and chatbots using Retrieval Augmented Generation (RAG) model…