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End-User Self-Service for Automated User Profile Creation in SageMaker Domains

As organizations expand their use of Amazon SageMaker to empower data scientists and machine learning (ML) engineers, managing access to development environments becomes a critical concern. In the last blog, we discussed how SageMaker Domains can provide isolated, secure, and fully-featured environments for users.

However, manually creating user profiles for every user quickly becomes a bottleneck—especially in large or fast-growing organizations. Asking users to submit an IT ticket and wait for days before it can be fulfilled is unacceptable in today's fast paced environment.

In this blog, we will describe how organizations use Rafay's GPU PaaS to provide their users with a self-service experience to onboard themselves into SageMaker Domains without waiting on IT or platform teams. This not only improves efficiency and user experience but also ensures consistency and compliance across the organization.

SageMaker AI Self Service

Why does End User Self-Service Matter?

There are several reasons why both Enterprise IT and end users prefer self service. Some of them are described below.

  1. Faster Onboarding for ML Practitioners

In a traditional model, provisioning a SageMaker user profile often involves tickets, approvals, and manual steps by DevOps or platform teams. This can delay productivity by days or weeks. With self-service automation, new users can create their own SageMaker user profiles through the self service portal in Rafay GPU PaaS .

This dramatically shortens onboarding time and allows data scientists to start building models immediately.

  1. Scalability Without Operational Overhead

As ML usage grows, the number of users that need access to SageMaker increases as well. Supporting this growth manually does not scale. Self-service systems automate the creation of user profiles based on predefined policies and templates, enabling organizations to onboard dozens—or even hundreds—of users with zero manual effort.

Admins do not have be clicking around setting 100s of options correctly.

  1. User Empowerment Without Sacrificing Control

Self-service does not mean lack of governance. On the contrary, administrators can define the parameters and constraints under which self-service provisioning occurs. For example, IAM roles, default instance types, shared data repositories, and lifecycle policies can be standardized across all profiles.

Users get the flexibility to start working when they want, while admins retain complete control over security, cost, and compliance.

How Users use Rafay for Self Service Access to a SageMaker Domain

With Rafay GPU PaaS , end user can get self-service access to an allowed SageMaker Domain in just 2 steps and in less than 2 minutes

Step 1: Access Self Service Portal

The administrator would have already enabled the self service card for SageMaker AI for the user. As you can see in the example below, the data scientist has logged into the Rafay GPU PaaS self-service portal, selects the SageMaker AI card and launches it.

SageMaker AI Self Service Card

Step 2: Access SageMaker AI Domain

In a couple of minutes, the automation backing the self-service card will create a user profile in SageMaker AI for the user in the selected SageMaker Domain. The end user is presented with the URL for SageMaker AI. The user just has to click on the URL, login into the AWS Console and perform their tasks in the organization's SageMaker Domain.

User Access

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Watch a brief video of the user experience below.

Conclusion

In today’s fast-paced enterprise ML environment, waiting for manual provisioning and on-boarding is no longer acceptable. Self-service automation for SageMaker Domain user profiles empowers teams, accelerates innovation, and maintains control through smart defaults and policy-based configurations. By embracing this approach, organizations can scale their AI initiatives without scaling operational overhead.