Category: Amazon SageMaker Unified Studio

Amazon SageMaker now supports account-agnostic project profiles, so you can create reusable project templates across multiple AWS accounts and organizational units. In this post, we demonstrate how account-agnostic project profiles can help you simplify and streamline the management of SageMaker project creation while maintaining security and governance features. We walk through the technical steps to configure account-agnostic, reusable project profiles, helping you maximize the flexibility of your SageMaker deployments.

In this blog post, we show you how Amazon Web Services (AWS) is simplifying data integration with zero-ETL while realizing performance benefits and cost optimizations. As organizations gather data for analytics and AI, they are increasingly finding themselves caught in a complex web of extract, transform, and load (ETL) pipelines—the traditional backbone of data integration. […]

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 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.

In this post, we explore its real-world impact through the lens of an ecommerce company striving to boost their bottom line. To illustrate this practical application, we walk you through how you can use the prebuilt integration between SageMaker Catalog and AWS Glue crawlers to automatically capture lineage for data assets stored in Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

In this post, we discuss the design and security controls in place when using generative SQL and its use in both Amazon SageMaker Unified Studio and Amazon Redshift Query Editor v2.

This post demonstrates how the new jobs experience works in SageMaker Unified Studio.

Today, we are excited to launch a new visual workflows builder in SageMaker Unified Studio. With the new visual workflow experience, you don’t need to code the Python DAGs manually. Instead, you can visually define the orchestration workflow in SageMaker Unified Studio, and the visual definition is automatically converted to a Python DAG definition that is supported in Airflow.This post demonstrates the new visual workflow experience in SageMaker Unified Studio.

In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools.