Category: Amazon SageMaker Unified Studio

Most of the cost of adopting something new isn’t the new thing, it’s the move. Moving a Hive table to Apache Iceberg has typically meant weeks of engineering work. Last week, Amazon S3 Tables turned that into three steps: start, sync as often as you need, complete. The source stays writable except for a few […]

Over the past week, we passed Laba festival, a traditional marker in the Chinese calendar that signals the final stretch leading up to the Lunar New Year. For many in China, it’s a moment associated with reflection and preparation, wrapping up what the year has carried, and turning attention toward what lies ahead. Looking forward, […]

Simplify AI experimentation with zero-infrastructure MLflow that launches in minutes, scales automatically, and seamlessly integrates with SageMaker’s model customization and pipeline capabilities.

Amazon SageMaker Unified Studio introduces new one-click onboarding experiences and serverless notebooks with a built-in AI agent without any manual set up or provisioning of your domain or compute resources. You can launch SageMaker Unified Studio directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, and Amazon S3 Tables console pages, giving a fast path to analytics and AI workloads.

Amazon SageMaker Catalog now offers column-level metadata forms and enforced glossary requirements, enabling organizations to improve data classification, discoverability, and governance through standardized business metadata.

AWS AI league is a program that helps organizations upskill their workforce by combining fun competition with hands-on learning using AWS AI services. It offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization, and prompt engineering – essential skills for building generative AI solutions.

Amazon SageMaker has introduced three new capabilities—Amazon QuickSight integration for dashboard creation, governance, and sharing, Amazon S3 Unstructured Data Integration for cataloging documents and media files, and automatic data onboarding from Lakehouse—that eliminate data silos by unifying structured and unstructured data management, visualization, and governance in a single experience.

Amazon S3 Vectors is a new cloud object store that provides native support for storing and querying vectors at massive scale, offering up to 90% cost reduction compared to conventional approaches while seamlessly integrating with Amazon Bedrock Knowledge Bases, SageMaker, and OpenSearch for AI applications.