Category: Artificial Intelligence

ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio.

In this post, we demonstrate how to integrate real-time monitoring with AI-powered remediation suggestions, combining Amazon Managed Grafana for visualization, Amazon Bedrock for intelligent response recommendations, and AWS Systems Manager for automated remediation actions on Amazon Web Services (AWS).

In this blog post, AWS introduces a solution to a common challenge in scientific research – the inefficient management of fragmented scientific data. The post demonstrates how the next generation of Amazon SageMaker, through its Unified Studio and Catalog features, helps scientists streamline their workflow by integrating data management and analytics capabilities.

In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making.

We’re excited to introduce the AWS Data Processing MCP Server, an open-source tool that uses the Model Context Protocol (MCP) to simplify analytics environment setup on AWS. In this post, we explore how the AWS Data Processing MCP Server accelerates analytics solution development and how data engineers can transform raw data into business-ready insights through AI-assisted workflows, significantly reducing development time and complexity.

We now have a public preview of two integrations between Amazon Simple Storage Service (Amazon S3) Vectors and Amazon OpenSearch Service that give you more flexibility in how you store and search vector embeddings. In this post, we walk through this seamless integration, providing you with flexible options for vector search implementation.

Amazon Web Services (AWS) and Collibra have built a new integrated solution that demonstrates the integration between the Collibra Platform and the next generation of Amazon SageMaker. In this post, we take a closer look at the integration, describe the use cases it enables, walk through the architecture, and show how to implement the solution in your environment.

Amazon OpenSearch Service is a versatile search and analytics tool. In this post, we explore conversational search, its architecture, and various ways to implement it.

In this post, we showcase how financial planners, advisors, or bankers can now ask questions in natural language. These prompts will receive precise data from the customer databases for accounts, investments, loans, and transactions. Amazon Bedrock Knowledge Bases automatically translates these natural language queries into optimized SQL statements, thereby accelerating time to insight, enabling faster discoveries and efficient decision-making.

In this post, we introduce authorization policies for custom asset types—a new governance capability in Amazon SageMaker that gives organizations fine-grained control over who can create and manage assets using specific templates. This feature enhances data governance by allowing teams to enforce usage policies that align with business and security requirements across the organization.