Category: Amazon SageMaker

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.

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 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.

In this post, we show you how EUROGATE uses AWS services, including Amazon DataZone, to make data discoverable by data consumers across different business units so that they can innovate faster. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

With Amazon SageMaker Lakehouse unified data connectivity, you can confidently connect, explore, and unlock the full value of your data across AWS services and achieve your business objectives with agility. This post demonstrates how SageMaker Lakehouse unified data connectivity helps your data integration workload by streamlining the establishment and management of connections for various data sources.

Amazon SageMaker Unified Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. This post demonstrates how SageMaker Unified Studio unifies your analytic workloads.