AWS Launches Simplified Login for SageMaker Studio, Offering a $100 Free Tier for New Users

ME AI message: Today, we are pleased to announce that new Amazon SageMaker AI customers can now enter a fully configured Amazon SageMaker Studio environment with just a few steps after registering with AWS. You can use your existing login methods, such as Amazon, Apple, GitHub, Google accounts, or email addresses. Most customers do not need to set up advanced AWS accounts, enter credit card information, or manually configure their environment. New customers also receive $100 in AWS Free Tier credits, which can be used for various SageMaker AI features, including model customization, training jobs, Studio notebooks, and endpoint deployment.
This article builds upon our previous post, "One-Click Access to Amazon SageMaker Studio from Hugging Face," which demonstrated how to directly enter SageMaker Studio workflows via deep links on Hugging Face model pages. Earlier this year, we reduced the time to create a Studio environment from two minutes to approximately 20 seconds and pre-configured model customization permissions in every new Studio domain. These improvements eliminated most barriers developers faced after entering Studio, but a major hurdle remained before even reaching Studio: creating an AWS account. New customers must verify their email address, provide contact information, validate their phone number, submit payment details, and select a support plan before they can begin building. For individual developers and small startup teams accustomed to logging in and starting development within minutes, this remains a significant barrier.
New Features
With this release, SageMaker Studio has been integrated into the updated AWS login and registration experience. This experience offers new options for creating and managing AWS accounts and streamlines collaboration workflows. New customers can create an account using their existing identity credentials and begin developing in SageMaker Studio immediately.
A brand-new login experience
New customers can now sign up in just 2 to 3 steps using their Google, GitHub, Apple, or Amazon accounts, or with their email address. Most customers do not need to complete advanced AWS account registration, verify a phone number, or enter credit card information:

Figure 1: AWS registration page offering social account login options

After registration, SageMaker AI will automatically create your Studio environment in approximately 20 seconds. You will then be directed to the page corresponding to your starting point. For example, you can discover and select a model on Hugging Face, then deploy it by clicking Deploy on SageMaker AI. Afterwards, Studio will open the deployment page with the model pre-configured for creating an endpoint:

Figure 2: Deploying Hugging Face models on Amazon SageMaker AI

Figure 3: Configure endpoint settings in SageMaker Studio

Earn up to $200 in Free Tier credits—most customers don’t need a credit card.
New customers receive $100 in AWS Free Tier credits, applicable to various SageMaker AI features, including endpoint deployment, model customization, Studio notebooks, and model training. Additionally, you may qualify for an extra $100 in credits. Credits are automatically applied to your usage charges; you’ll receive an email notification when you’re nearing your limit. The Free Tier lasts for 6 months or until all credits are used, whichever comes first. At that point, resource usage stops automatically, so you won’t incur unexpected charges. You can upgrade to a paid plan at any time by adding payment information.
Building on previously released achievements
The environment created through the new login experience includes improvements from previous releases. Permissions required for model customization, training, evaluation, and deployment are preconfigured, so you do not need to create an AWS Identity and Access Management (IAM) role. Eligible accounts have default access to G5, G6, and G4dn GPU instances for endpoints, training jobs, and Studio notebooks. Additionally, the instance selection menu in Studio displays quota availability, allowing you to quickly see which instance types are available.
Get started
You can immediately experience the new login method:

Browse models on Hugging Face, select Deploy, choose Amazon SageMaker AI, then select Customize on SageMaker AI or Deploy on SageMaker AI for supported models. You can also visit the Amazon SageMaker Studio page and select Get started with Amazon SageMaker AI.
On the AWS login page, select Google, GitHub, Apple, or Amazon, or enter your email address and follow the prompts to create an account in a few simple steps.
Enter the fully configured SageMaker Studio environment and start developing using your Free Tier credits.

A new login experience is available for new AWS accounts. If you already have an AWS account, your login method will not change.
Conclusion
The new login experience removes the last major barrier between discovering Amazon SageMaker AI and starting to build. With faster environment creation, pre-configured permissions, deep links from Hugging Face, and default GPU access, individual developers can get started quickly. Startup teams can now use their existing identity accounts to complete the process from finding a model to customizing or deploying it in just minutes.
To get started, visit aws.amazon.com/sagemaker/studio or explore models on Hugging Face.

Author Bio

Hazim Qudah
Hazim is a Professional Solutions Architect at AWS, based in Dallas, Texas, USA. He enjoys helping customers build and adopt AI/ML solutions using AWS technologies and best practices. Before joining AWS, he spent years working in technology consulting, serving clients across numerous industries and regions. In his free time, he enjoys running and playing with his dog, Nala.

Paul Hargis
Paul has focused on machine learning at companies including AWS, Amazon, and Hortonworks. He enjoys building technical solutions and teaching others how to use them. Paul is passionate about helping customers scale their machine learning initiatives to solve real-world problems. Before joining AWS, he served as Chief Architect for Amazon Exports and Expansions, helping improve the shopping experience for international customers on amazon.com.

Naidile Murali
Naidile is a product manager at AWS, based in Bellevue, Washington, USA. She primarily focuses on enhancing the AI/ML developer experience on Amazon SageMaker, including new customer onboarding in Studio, the SageMaker JumpStart model hub, and GPU capacity management in IDEs. Prior to joining AWS, she worked as a software engineer at HSBC and holds an MBA from Georgetown University.

(Source: MLion)