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Why Enterprises Should Use Domains for SageMaker AI

As organizations continue to invest in artificial intelligence (AI) and machine learning (ML) to drive digital transformation, the demand for streamlined, secure, and scalable development environments has never been greater.

Many organizations that are standardized on Amazon AWS may use Amazon SageMaker AI to build, train, and deploy machine learning models at scale with minimal operational overhead. SageMaker AI provides a fully managed environment that streamlines the entire ML lifecycle, enabling faster innovation, stronger governance, and cost-effective AI development.

In this introductory blog, we will describe one of the most critical capabilities of SageMaker AI called Domains. In the next blog, we will describe how organizations can scale their AI/ML teams by providing their data scientists and ML engineers with a self service experience for access to SageMaker Domains.

SageMaker AI Logo

What is a SageMaker Domain?

A SageMaker Domain is a managed environment that provides a centralized way to organize, manage, and govern machine learning activities for users across an organization. Domains enable administrators to configure shared data sources, assign user profiles, enforce security policies, and maintain visibility over ML assets such as notebooks, datasets, models, and pipelines.

SageMaker AI Domain

Centralized Governance and Security

One of the most critical needs in enterprise AI adoption is strong governance. A SageMaker Domain allows enterprises to enforce organization-wide security policies and access controls.

For example, administrators can define granular permissions per user or group. This ensures that sensitive datasets and models are only accessible to authorized personnel, helping enterprises stay compliant with data privacy regulations like GDPR, HIPAA, and SOC 2. Domains also support single sign-on (SSO) making it easier for enterprises to integrate with their corporate identity providers. This unified access control improves the overall security posture while enhancing user convenience.

Another commonly used control in domains is to ensure that the users in the domain can only use certain allowed instance types. In the image below, users in the SageMaker Domain will only be able to launch and use "ml.t3.large" and "ml.t3.xlarge" instance types.

SageMaker AI Domain Rules

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There are a number of controls that can be enforced on all users in a SageMaker Domain.

Scalable and Isolated User Environments

In traditional settings, managing isolated and consistent environments for multiple data scientists can be a logistical nightmare. SageMaker Domains solve this by allowing each user to have a User Profile within the Domain. These profiles are fully isolated and come with dedicated storage, compute resources, and custom configurations.

This design enables team members to experiment independently without impacting others, while administrators can still centrally monitor usage and performance. It also supports reproducibility and version control, both of which are essential for scaling ML practices across teams.

SageMaker AI User Profiles

Collaboration and Lifecycle Management

Collaboration is key in enterprise AI initiatives. Domains offer shared spaces for teams to collaborate on notebooks, training jobs, and models. Features like shared Projects and Model Registry promote reusability and prevent duplication of effort.

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SageMaker Domains also integrate seamlessly with the full ML lifecycle, from data labeling and feature engineering to model training, deployment, and monitoring. With built-in tools like SageMaker Studio, pipelines, and experiments, organizations can standardize workflows and accelerate time-to-value for ML projects.

Cost and Resource Optimization

Enterprises often struggle with cost overruns and resource sprawl in unmanaged ML environments. Domains help address this through built-in monitoring, budgeting, and usage tracking capabilities. Admins can set up policies to automatically shut down idle resources, ensuring efficient use of infrastructure. Additionally, the modular nature of SageMaker Domains allows for efficient provisioning of compute resources only when needed—whether it’s for training a large model or running a quick batch inference job.

Conclusion

For organizations looking to scale AI/ML efforts while maintaining control, security, and efficiency, SageMaker Domains are a foundational building block. They bring together the best of user experience, governance, and operational efficiency in a way that aligns perfectly with enterprise needs. If you’re serious about enterprise-grade AI with Amazon SageMaker AI, it’s time to start using Domains in SageMaker.

In the next blog, we will describe how organizations use Rafay's PaaS to provide their end users (i.e. data scientists and ML engineers) with a self service experience for access to user profiles in a SageMaker Domain.