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AWS Solutions Library

Guidance for Developing a Data & AI Foundation with Amazon SageMaker

Accelerate building Data, Analytics, AI, and Visualization applications on AWS using Terraform modules for next-gen Amazon SageMaker platform

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Overview

This Guidance demonstrates how to accelerate data and AI foundation development on AWS through infrastructure-as-code and application modules. It helps organizations rapidly deploy Iceberg data lakes and Amazon SageMaker service while maintaining enterprise-grade security and governance standards. The solution shows how to implement proven architectural patterns for data management, enabling teams to focus on innovation rather than infrastructure setup. Furthermore, it demonstrates how to quickly establish data pipelines and products through a modular approach, empowering organizations to drive value from their data assets while ensuring scalability, security, and compliance.

Benefits

Break down data silos using Amazon SageMaker's lakehouse zero-ETL integrations and federated queries. Access all your data from one place while building AI applications faster.

Deploy enterprise-grade data platforms with built-in security and compliance using Infrastructure-as-Code modules. Amazon SageMaker Catalog automatically tracks data lineage and quality metrics.

Empower producers and consumers to share curated data assets securely through dedicated projects. Amazon SageMaker Unified Studio provides integrated tools for analytics, ML, and generative AI development.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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