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It’s been nearly a decade since Amazon Web Services (AWS), Amazon’s cloud computing division, announced SageMaker, its platform for creating, training, and deploying AI models. While in previous years AWS has focused on dramatically expanding SageMaker’s capabilities, simplification was the goal this year.
At its re:Invent 2024 conference, AWS unveiled SageMaker Unified Studio, a single place to find and work with data from across the organization. SageMaker Unified Studio combines tools from other AWS services, including existing SageMaker Studio, to help customers discover, prepare, and manipulate data to build models.
“We are seeing a convergence between analytics and AI, as customers use data in increasingly interconnected ways,” Swami Sivasubramanian, vice president of data and AI at AWS, said in a statement. “SageMaker Next Generation combines capabilities to provide customers with all the tools they need to process data, develop and train machine learning models, and generative AI, right within SageMaker.”
With SageMaker Unified Studio, customers can publish and share data, models, applications, and other items with members of their team or the broader organization. The service exposes data security controls and adjustable permissions, as well as integrations with AWS’ Bedrock Model Development Platform.
AI is built into SageMaker Unified Studio — specifically, Q Developer, Amazon’s programming chatbot. In SageMaker Unified Studio, a Q Developer can answer questions like “What data should I use to get a better idea of product sales?” Or “Create SQL to calculate total revenue by product category.”
“A Q developer (can) support development tasks such as data discovery, coding, SQL generation, and data integration” in SageMaker Unified Studio, AWS explained in a blog post.
Beyond SageMaker Unified Studio, AWS has launched two small additions to its SageMaker product family: SageMaker Catalog and SageMaker Lakehouse.
SageMaker Catalog allows administrators to define and enforce access policies for AI applications, models, tools, and data in SageMaker using a single permission model with fine-grained controls. Meanwhile, SageMaker Lakehouse provides SageMaker connections and other tools for data stored in AWS data lakes, data warehouses, and enterprise applications.
AWS says SageMaker Lakehouse works with any tools compatible with the Apache Iceberg standard — Apache Iceberg is an open source format for large analysis tables. Administrators can apply cross-data access controls in all analytics and AI tools used by SageMaker Lakehouse, if desired.
In a somewhat related development, SageMaker should now work better with SaaS applications, thanks to new integrations. SageMaker customers can access data from applications like Zendesk and SAP without having to extract, transform, and load that data first.
“Customers may have data spread across multiple data lakes, as well as a data warehouse, and would benefit from a simple way to unify all of this data,” AWS wrote. “Now, customers can use their favorite analytics and machine learning tools on their data, regardless of how and where it is physically stored, to support use cases including SQL analytics, ad hoc querying, data science, machine learning, and generative AI.”
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