Category: Storage

Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.

This post outlines a custom multilingual document extraction and content assessment framework using a combination of Anthropic’s Claude 3 on Amazon Bedrock and Amazon A2I to incorporate human-in-the-loop capabilities.

In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.

In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.

In this post, we explore three scenarios demonstrating the versatility of integrating Amazon EFS with SageMaker Studio. These scenarios highlight how Amazon EFS can provide a scalable, secure, and collaborative data storage solution for data science teams.

In this post, we demonstrate how to simplify data access to Amazon S3 from SageMaker Studio using S3 Access Grants, specifically for different user personas using IAM principals.

In this blog post, we show you how Amazon Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.

In this post, we demonstrate a solution using Amazon FSx for NetApp ONTAP with Amazon Bedrock to provide a RAG experience for your generative AI applications on AWS by bringing company-specific, unstructured user file data to Amazon Bedrock in a straightforward, fast, and secure way.

In this post, we show you how to use Amazon Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With […]