Category: Artificial Intelligence

The AI search flow builder is available in all AWS Regions that support OpenSearch 2.19+ on OpenSearch Service. In this post, we walk through a couple of scenarios to demonstrate the flow builder. First, we’ll enable semantic search on your old keyword-based OpenSearch application without client-side code changes. Next, we’ll create a multi-modal RAG flow, to showcase how you can redefine image discovery within your applications.

Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.

We’re excited to introduce a new enhancement to the search experience in Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker—exact match search using technical identifiers. In this post, we demonstrate how to streamline data discovery with precise technical identifier search in Amazon SageMaker Unified Studio.

In this blog post, we will demonstrate how business units can use Amazon SageMaker Unified Studio to discover, subscribe to, and analyze these distributed data assets. Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems.

We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started.

In this post, we demonstrate how Amazon Redshift Serverless AI-driven scaling and optimization impacts performance and cost across different optimization profiles.

As we enter 2025, OpenSearch Service support for OpenSearch 2.17 brings these improvements to the service. In this post, we walk through 2024’s innovations with an eye to how you can adopt new features to lower your cost, reduce your latency, and improve the accuracy of your search results and generated text.

In this post, you’ll learn how to use OpenSearch Service and Amazon Bedrock to build AI-powered search and generative AI applications. You’ll learn about how AI-powered search systems employ foundation models (FMs) to capture and search context and meaning across text, images, audio, and video, delivering more accurate results to users. You’ll learn how generative AI systems use these search results to create original responses to questions, supporting interactive conversations between humans and machines.

In this post, we discuss the foundational building blocks of SageMaker Unified Studio and how, by abstracting complex technical implementations behind user-friendly interfaces, organizations can maintain standardized governance while enabling efficient resource management across business units. This approach provides consistency in infrastructure deployment while providing the flexibility needed for diverse business requirements.

OpenSearch Service provides rich capabilities for RAG use cases, as well as vector embedding-powered semantic search. You can use the flexible connector framework and search flow pipelines in OpenSearch to connect to models hosted by DeepSeek, Cohere, and OpenAI, as well as models hosted on Amazon Bedrock and SageMaker. In this post, we build a connection to DeepSeek’s text generation model, supporting a RAG workflow to generate text responses to user queries.