Monthly Archives: December 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE) of their choice, while having access to the SageMaker Studio resources and tooling across the IDEs. This updated user experience (UX) provides data scientists, data engineers, and ML engineers more choice on where to build and train their ML models within SageMaker Studio. As a web application, SageMaker Studio has improved load time, faster IDE and kernel start up times, and automatic upgrades.

In addition to managed JupyterLab and RStudio on Amazon SageMaker, we have also launched managed Visual Studio Code open-source (Code-OSS) with SageMaker Studio. Once a user selects Code Editor and launches the Code Editor space backed by the compute and storage of their choice, they can take advantage of the SageMaker tooling and Amazon Toolkit, as well as integration with Amazon EMR, Amazon CodeWhisperer, GitHub, and the ability to customize the environment with custom images. As they can do today with JupyterLab and RStudio on SageMaker, users can switch the Code Editor compute on the fly based on their needs.

Lastly, in order to streamline the data science process and avoid users having to jump from the console to Amazon SageMaker Studio, we added the ability to view Training Jobs and Endpoint details in the SageMaker Studio user interface (UI) and have enabled the ability to view all running instances across launched applications. Additionally, we improved our Jumpstart foundation models (FMs) experience so users can quickly discover, import, register, fine tune, and deploy a FM.

Solution overview

Launch IDEs

With the new version of Amazon SageMaker Studio, the JupyterLab server is updated to provide faster startup times and a more reliable experience. SageMaker Studio is now a multi-tenant web application from where users can not only launch JupyterLab, but also have the option to launch Visual Studio Code open-source (Code-OSS), RStudio, and Canvas as managed applications. The SageMaker Studio UI enables you to access and discover SageMaker resources and ML tooling such as Jobs, Endpoints, and Pipelines in a consistent manner, regardless of your IDE of choice.


SageMaker Studio contains a default private space that only you can access and run in JupyterLab or Code Editor.


You also have the option to create a new space in SageMaker Studio Classic, which will be shared with all the users in your domain.

Enhanced ML Workflow

With the new interactive experience, there’re significant enhancements and a simplification of parts of the existing ML workflow from Amazon SageMaker. Specifically, within Training and Hosting there’s a much more intuitive UI-driven experience to create new jobs and endpoints while also providing metric tracking and monitoring interfaces.

Training

For training models on Amazon SageMaker, users can conduct training of varying flavors whether that is through a Studio Notebook through a Notebook Job, a dedicated Training Job, or a fine-tuning job via SageMaker JumpStart. With the enhanced UI experience, you can track past and current training jobs utilizing the Studio Training panel.

You can also toggle between specific Training Jobs to understand performance, model artifacts location, and also configurations such as the hardware and hyperparameters behind a training job. The UI also gives the flexibility to be able to start and stop training jobs via the Console.

Hosting

There are a variety of different Hosting options within Amazon SageMaker as well that you can utilize for model deployment within the UI. For creating a SageMaker Endpoint, you can go to the Models section where you can utilize existing models or create a new one.

Here you can utilize either a singular model to deploy an Amazon SageMaker Real-Time Endpoint or multiple models to work with the Advanced SageMaker Hosting options.

Optionally for FMs, you can also utilize the Amazon SageMaker JumpStart panel to toggle between the list of available FMs and either fine-tune or deploy through the UI.

Setup

The updated Amazon SageMaker Studio experience is launching alongside the Amazon SageMaker Studio Classic experience. You can try out the new UI and choose to opt-in to make the updated experience the default option for new and existing domains. The documentation lists the steps to migrate from SageMaker Studio Classic.

Conclusion

In this post, we showed you the features available in the new and improved Amazon SageMaker Studio. With the updated SageMaker Studio experience, users now have the ability to select their preferred IDE backed by the compute of their choice and start the kernel within seconds, with access to SageMaker tooling and resources through the SageMaker Studio web application. The addition of Training and Endpoint details within SageMaker Studio, as well as the improved Amazon SageMaker Jumpstart UX, provides a seamless integration of ML steps within the SageMaker Studio UX. Get started on SageMaker Studio here.

About the Authors

Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. She helps customers optimize their machine learning workloads using Amazon SageMaker.

Ram Vegiraju is a ML Architect with the SageMaker Service team. He focuses on helping customers build and optimize their AI/ML solutions on Amazon SageMaker. In his spare time, he loves traveling and writing.

Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has a decade of experience in DevOps, infrastructure, and ML. She is also the author of a book on computer vision. In her spare time, she enjoys traveling and hiking.

Khushboo Srivastava is a Senior Product Manager for Amazon SageMaker. She enjoys building products that simplify machine learning workflows for customers, and loves playing with her 1-year old daughter.

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As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities, whereas data scientists perform ML training and validation activities. Another challenge is the effort required to set up and manage the networking configurations. Typically, there is no simple mechanism for administrators to discover, implement, and manage the right networking and security configurations their teams need.

That’s why today we are excited to announce the new onboarding experience that makes it effortless for you to set up Amazon SageMaker domains for your organization. As a platform administrator, you can use the updated user interface (UI) and APIs to onboard users faster, with the right security settings and infrastructure.

Let’s see what’s new and how to get started!

Introducing the SageMaker domain setup UI for organizations

The new UI for organizations lets you set up a SageMaker domain via the AWS Console and onboard users and organizations with just a few clicks. The redesigned UI guides you through the setup and provides step-by-step instructions so that you can scale quickly. You can choose between using AWS Identity Access Management (IAM) or AWS IAM Identity Center authentication and map scoped-down policies to your existing groups or users. You can assign existing roles or create new ones based on their typical ML activities. An ML activity represents a set of permissions for a specific task, such as running ML training jobs.

In addition to setting up and configuring your SageMaker apps and execution roles, the new experience offers an updated UI for implementing complex networking configuration, such as VPC endpoints, subnets and security groups, and encryption settings. You can also manage your subnets and connection modes later on if changes are required.

Now let’s go through the new experience in more depth.

Prerequisites

Before you use the advanced setup for organizations, you need to have the following:

• An AWS account
• An IAM role with permissions to create the resources needed to set up a SageMaker domain

Set up a SageMaker domain for organizations

To experience the updated UI, the ML admin completes the following steps:

1. On the SageMaker console, choose Set up for organizations.
   
   This takes you to the Set up SageMaker Domain wizard, where the Set up for organizations option is already selected.
2. Choose Configure.
   
3. On the Domain details page, enter a domain name, then choose Next.
   
4. On the Users and ML Activities page, select your preferred authentication method. For this post, we select AWS Identity Center. Note that your AWS Identity Center setup must be in the same Region as where you are creating your SageMaker domain.
   
5. In the Who will use Studio? section, you can optionally choose user groups to grant access to the SageMaker domain.
   
6. Select Create a new role to create a new role to assign activities to, or use an existing role. For ML activities, select from the list of predefined activities.
   
7. In the S3 Bucket Access section, enter an Amazon Simple Storage Service (Amazon S3) bucket that all the domain users will have access to, then choose Next. You can specify more than one S3 bucket.
   
8. On the Applications page, you can specify and configure the integrated development environments (IDEs) available under the SageMaker domain. For SageMaker Studio, select the updated or classic version. You can also configure Canvas, Code Editor, and RStudio.
   
9. Choose Next.
10. On the Network page, select to use VPC only or public internet access. For this post, we select Virtual Private Cloud (VPC) Only. If you’re using a VPC, specify your VPC, subnets, and security groups, then choose Next.
    
11. On the Storage page, you can optionally set an encryption key.
12. You can also optionally configure the default and maximum space size for the Amazon Elastic Block Store (Amazon EBS) volume for the Amazon Elastic Compute Cloud (Amazon EC2) instance that hosts the JupyterLab and Code Editor.
    
13. Choose Next.
14. On the Review and create page, review your configurations, then choose Submit to create the domain.
    
    
15. This starts the process of setting up the SageMaker domain, which takes 2–4 minutes to complete.
    
16. When the domain is ready, a success banner appears.
    

New: Update existing domains for organizations

Now that we have gone through the user journey of an admin setting up a new SageMaker domain for organizations, the domain is ready and ML users can be onboarded to SageMaker. This process is not a one-time event; after creating the domains, the requirements may evolve and updates to the domain configuration are needed. Let’s explore some newly launched features as part of this setup that allow updates to existing domains.

Prerequisites to update domains

To use these new features, the ML admins must have access to:

• An AWS account.
• An IAM role with permissions to create the resources needed to set up a SageMaker domain.
• A SageMaker domain in your selected AWS account.
• The AWS Command Line Interface (AWS CLI). To install the AWS CLI, refer to Install or update the latest version of the AWS CLI.

Update a subnet in an existing domain via the AWS CLI

As organizations scale the adoption of ML, their needs evolve, which requires changes in their infrastructure. As you add more users and resources to your projects and teams, you require more resources (such as IP range and endpoints). You may also want to isolate a few subnets and disassociate these subnets from SageMaker Studio and therefore want to remove the subnets from your domains. One of the challenges admins face when you want to add or remove subnets is that updating the subnets of a domain requires expertise and time. We’re excited to announce that we have simplified this process, and ML admins now can update the subnets of a domain via the AWS CLI.

Let’s walk through this functionality.

In this example use case, you have created a new SageMaker Studio domain with two subnets: subnet-1 and subnet-2. You have exhausted all the domain subnet IPs and now want to add new subnets subnet-3 and subnet-4 to the domain. See the following code:

# Update Domain with a new Subnet being added
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker update-domain --domain-id $DOMAIN_ID --subnet-ids '["subnet-1","subnet-2","subnet-3", "subnet-4"]'

# Describe the Domain to see if the Domain Subnet list got updated
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker describe-domain --domain-id $DOMAIN_ID

If you realize that you don’t actually need so many IPs, you can remove a subnet (for this example, subnet-4) from the existing list of subnets. See the following code:

# Update Domain with a Subnet being removed
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker update-domain --domain-id $DOMAIN_ID --subnet-ids '["subnet-1","subnet-2","subnet-3"]'

# Describe the Domain to see if the Domain Subnet list got updated
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker describe-domain --domain-id $DOMAIN_ID

Change your network connection mode in an existing domain via the AWS CLI

When you’re conducting tests or exploring SageMaker to learn more about the service, you might create your domain with public internet access. However, as you set up projects and scale your ML workloads, you may need to change your authentication mode to VPC only to be compliant with your organization’s existing network and security requirements. We’re excited to announce that ML admins now can change their network connection mode from public internet to VPC only mode via the AWS CLI.

For example, in the following code, we update the domain AppNetworkAccessType to VpcOnly:

# Update Domain App Network Access type
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker update-domain --domain-id $DOMAIN_ID --app-network-access-type VpcOnly

In the following code, we update the domain AppNetworkAccessType to PublicInternetOnly:

# Update Domain App Network Access type
aws --region $REGION --endpoint-url $SAGEMAKER_ENDPOINT sagemaker update-domain --domain-id $DOMAIN_ID --app-network-access-type PublicInternetOnly

Conclusion

The new UI for organizations to set up domains and the new features related to updating existing domains are available today at no additional charge in all AWS Regions where SageMaker is available, except for the AWS GovCloud and AWS China Regions.

Try out these new features and let us know what you think. We always look forward to your feedback! You can send it through your usual AWS Support contacts or post it on the AWS Forum for SageMaker.

To learn more, visit New onboarding experience in SageMaker and check Onboard to Amazon SageMaker Domain using IAM Identity Center.

About the authors

Ozan Eken is a Senior Product Manager at Amazon Web Services. He is passionate about building onboarding products with the right infrastructure, security guardrails and governance for SageMaker. Outside of work, he likes exploring different outdoor activities and watching soccer.

Vikesh Pandey is a Machine Learning Specialist Solutions Architect at AWS, helping customers from financial industries design and build solutions on generative AI and ML. Outside of work, Vikesh enjoys trying out different cuisines and playing outdoor sports.

Anastasia Tzeveleka is a Machine Learning and AI Specialist Solutions Architect at AWS. She works with customers in EMEA and helps them architect machine learning solutions at scale using AWS services. She has worked on projects in different domains including Natural Language Processing (NLP), MLOps and Low Code No Code tools.

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