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Personalizing wellness recommendations at Calm with Amazon Personalize

This is a guest post by Shae Selix (Staff Data Scientist at Calm) and Luis Lopez Soria (Sr. AI/ML Specialist SA at AWS). Today, content is proliferating. It’s being produced in many different forms by a host of content providers, both large and small. Whether it’s on-demand video, music, podcasts, or other forms of rich…



This is a guest post by Shae Selix (Staff Data Scientist at Calm) and Luis Lopez Soria (Sr. AI/ML Specialist SA at AWS).

Today, content is proliferating. It’s being produced in many different forms by a host of content providers, both large and small. Whether it’s on-demand video, music, podcasts, or other forms of rich media, consumers are overwhelmed by choice on a regular basis. This is why Calm is on a mission to make the world happier and healthier, and is achieving this goal by delivering its top-rated app for meditation and sleep to consumers users around the world.

Impressively, Calm was named the “App of the Year” in 2017 after almost 10 million signups with a team of fewer than 20. As Calm scaled, so has their mission to diversify their content library beyond meditation and sleep stories.

Well-known celebrities, such as Lebron James, Harry Styles, and Ariana Grande, have contributed to a 10-times expansion of Calm’s content library. With this growth in content, customers have shared that it has become more challenging to find the content that is right for them. To address this, and to support customers’ expectations for personalized content, the team at Calm turned to Amazon Personalize.

Based on over 20 years of personalization experience at, Amazon Personalize enables you to improve customer engagement by powering personalized product and content recommendations and targeted marketing promotions. Amazon Personalize uses machine learning (ML) to create higher-quality recommendations for your websites and applications. You can get started without any prior ML experience and use simple APIs to easily build sophisticated personalization capabilities in just a few clicks.

Like other content streaming providers such as Amazon Prime Video, Calm embarked on a journey to provide in-app recommendations to help users discover the content best suited to their needs and interests. As a startup with a small ML team, Calm found that Amazon Personalize provided a powerful platform to launch their first set of recommendations. Deploying recommendations and collecting user feedback was much faster with Amazon Personalize than building a deep learning system from scratch.

The recommendations generated from Amazon Personalize led to a 3.4% increase in daily mindfulness practice among Calm’s members, a significant lift considering this custom metric encompasses various factors across engagement, retention, and customer personas. Each day, 100,000 people download Calm to start their meditation journeys. These results enable Calm to achieve their goal of helping people develop a mindfulness habit.

Calm’s content is unique and tailored for their audience

Unlike other content platforms, where content creators can submit content freely, Calm sets a high bar for the content featured on their platform. Calm’s mindfulness and creative teams record and carefully curate content to reflect what end users are looking for.

For example, some users engage with Calm for Sleep Stories only and have no interest in Soundscapes, or Meditations while sitting in a quiet place focused on the task at hand, versus Music that is often listened to while driving or studying.

Alternatively, users come to Calm to learn how to meditate, a new skill that must be learned in an orderly way. This means starting with short, guided meditations with explanations and reminders to focus on the present moment. More experienced meditators often prefer silence, other than the ring of a bell to indicate how much time has passed.

Calm’s beginner content, like “7 Days of Calm” by Tamara or “How to Meditate” by Jeff Warren, is very popular, but entry-level sessions aren’t necessarily relevant recommendations for an experienced meditator. Conversely, a beginner may not be ready for the longer, less guided content.

In summary, Calm’s uniqueness is that while many digital applications are optimizing their experiences to get users to engage for longer periods of time, Calm’s goal is to help users form healthy habits, which may lead to less time using their app.

Calm’s mission, along with the idiosyncrasies in how content is consumed compared to other large applications, leads to a careful and considered approach to recommendations, not simply emulating what others have done.

Launching personalized recommendations at Calm

Calm’s first foray into recommended content was with Sleep Stories. Although meditations might need to be learned in a specific order, Sleep Stories are often interchangeable and users develop clear preferences (for example, for deeper-voiced narrators or stories about nature). Calm began its personalization journey by offering the most popular content while removing previously listened-to stories.

This simple approach allowed the small team to place recommendations in front of users without the need to train and deploy an ML model. However, users stopped engaging with this feature over time. Although the recommended stories were often the most clicked, they were rarely completed, meaning users wanted to try the recommendations but often switched to other stories that were more relevant to their needs. This indicated that a more sophisticated approach could fill a real user need.

Unlike static, rule-based personalization systems, ML-powered personalization can deliver personalization at scale and works best with large volumes of data. Calm, with over 100 million app downloads, operates at a scale where they can leverage deep learning based approaches.

However, the complexity of training, tuning, and provisioning the necessary infrastructure and deploying the model was a daunting challenge for a small team. This is why Calm chose Amazon Personalize, to enable their team to quickly get started with a highly scalable ML-based recommender engine without the need of a big upfront investment.

When Calm looked at what it would take to build the equivalent of Amazon Personalize in-house, they found they would need the following:

  • A big data processing service to pull all historical data and split it into training and testing sets
  • GPU-enabled servers to train deep learning models, plus all the effort needed to maintain that hardware
  • Deep learning code to define and maintain recommender algorithms and models
  • Experimentation frameworks and infrastructure to evaluate different model architectures and hyperparameter tuning
  • A large-scale scoring pipeline to score recommendations for millions of users a day
  • Additional infrastructure to deploy and host real-time endpoints for data ingestion and inference

Additionally, these components needed to be integrated with Calm’s backend systems to display and test recommendations in mobile clients.

At large companies like Amazon, each of these components is handled by a different team. To avoid the complexity of doing it in-house and take advantage of features used by companies at this scale, Calm decided to try Amazon Personalize, a fully managed AI recommendations service.

Defining Calm’s Amazon Personalize Model

The Amazon Personalize user-personalization recipe was a good fit for Calm’s use case. As a sequence model, the algorithm can learn the step-by-step process Calm’s users follow to learn meditation. We also know that new content, like a new Sleep Story from Harry Styles, is very popular with users when first added, and Amazon Personalize can capture quick shifts in user preferences using the real-time event tracker.

Given the fit of the architecture, the complexity of building such a model in house, and the fact that Calm’s data already lived in AWS, Calm moved forward with Amazon Personalize.

Before planning the technical architecture of our Amazon Personalize deployment, we defined our problem in terms of our business objective and set a clear path on how we could achieve it.

The main business objective was to increase user engagement. Calm’s north star metric is the average number of days that it takes users to complete content. Calm uses this metric rather than the total number of complete audio listens because many users did 10 minutes of meditation a day, which is considered a positive signal towards a wellness-focused lifestyle. Calm isn’t trying to get users to engage for hours a day; that would be counter to its mental health mission.

In addition to increasing this key user engagement metric, a secondary business objective was to help users discover new content. Many of Calm’s users listen to the same Sleep Story or Soundscape they love over and over. Consequently, the number of repeat listens was limited in the training set so that the Amazon Personalize model didn’t simply recommend what was listened to previously.

Amazon Personalize has three main operational layers:

  • Data layer –Customers ingest three types of data (interactions, items, and users metadata) in batch from Amazon Simple Storage Service (Amazon S3) or incrementally using PUT APIs.
  • Training layer – Customers are able to pick algorithms, also known as recipes (user-personalization, personalize-ranking, and related items), which address specific use cases.
  • Inference layer – Customers can use their custom private models (solution versions), to infer for recommendations. Amazon Personalize provides the ability to infer in real time using managed endpoints known as campaigns, or through batch jobs from Amazon S3.

Let’s explore Calm’s Amazon Personalize data layer setup:

  • Interactions – A typical customer sequence for audio engagement could be: meditating with the Daily Calm, drifting off to Harry Style’s Sleep Story, and grooving to Moses Sumney’s Transfigurations. Each of these listens represents a data point in the interactions dataset. Additionally, we’re providing Amazon Personalize with other event types such as fully completed and favorited interactions; providing stronger signals than incomplete listens or item views.
  • Items – Some examples of metadata fields of content features are the narrator (Tamara has quite a fanbase, but other listeners swear by Jeff Warren), the length of the audio track, and when the track was added. All these features are useful for the model to understand and pick signals based on customer behavior (interactions). These features are also available for filtering at inference time.
  • Users – Calm uses the user’s account tenure, favorite time of day to use the app, and the country where a user engages the app as metadata. These metadata features are important at training time to enhance the personalization experience for each of these users. Amazon Personalize limits the user metadata size to five columns, which why this dataset is very specific to factors that could influence decision-making across the whole user’s base.

Given that Calm’s use case is focused on delivering personalized recommendations to users, we chose the HRNN-Metadata recipe. Although the content library isn’t as large as other content platforms, Calm’s audio content is tagged with detailed metadata, like voice depth, topic (such as anxiety, self-care, or sleep), whether it’s good for kids, and more. We decided to train a popularity-count solution version with every new model training cycle in order to have a baseline with Amazon Personalize offline metrics.

Calm’s architecture implementation

Prior to implementing our Amazon Personalize deployment, our data engineering team at Calm had already invested a lot of resources deploying analytics solutions on the AWS Cloud. We used Amazon Redshift as a central data warehouse and Amazon S3 as a scalable data lake, merging all application data with events and payments. An Airflow instance, running on Kubernetes, schedules and orchestrates all ETLs and ML pipelines. From a training data volume and runtime perspective, Amazon Personalize was more complex than previous ML pipelines.


The following diagram illustrates the training workflow.

To train a model in Amazon Personalize, the service needs the following resources:

  • A dataset group, which is the highest level of abstraction in the service, allowing you to encapsulate experiments
  • Datasets (interactions, users, items), which is where you can create each of the schemas and import your data into the service
  • Solutions configured with the recipes and hyperparameters to be used to create your solution versions (models)

We utilized the Boto3 Python SDK to configure and monitor the service. All the required training data for interactions (user audio listens), users (metadata on our users), and items (metadata on content) were already regularly updating in Amazon Redshift. To fit it into a form (schema) that Amazon Personalize could consume, a series of SQL transforms unload the three datasets to Amazon S3. We used Jinja2 to parameterize our queries so we could easily adjust how far back to look for training interactions, which types of users to include, and which metadata fields to leverage.

After the datasets and schema are exported to Amazon S3, we create a new dataset group, datasets, and import jobs for the model. We do this so each pipeline call is idempotent, and doesn’t require external configuration to proceed. After the solution version begins to train, the Airflow task runs a DescribeSolutionVersion call every 15 minutes to check training status. When it’s complete, Airflow posts the solution metrics to Slack so we can monitor, and saves all the training details and ARNs to Amazon S3.

Generating Calm recommendations: Batch inference

The following diagram illustrates the batch inference workflow.

We generate batch recommendations for each of our users every 2 days, requesting the upper limit (500) items recommendations for each user. Recent activity, subscription status, and active language determine who receives these recommendations. Amazon Personalize outputs batch recommendations as JSON lines, one row per user. Originally, we had a Python module convert the JSON lines to tabular format and load them into Amazon Redshift, but eventually realized the Amazon Redshift COPY command could handle the JSON lines directly, using JSON paths. This saved us an additional step, and allowed us to move some of the compute tasks from our Kubernetes cluster to Amazon Redshift.

One advantage of using batch recommendations instead of the real-time campaigns is that we could encode complex business logic into the recommendations before displaying them in front of users, beyond what is possible with Amazon Personalize filters. Our business rule engine performs the following logic:

  1. Aggregates individual audio recommendations to collections (like “7 Days of Calm”).
  2. Filters all recommendations to specific types, such as just Meditations, or only new content.
  3. Shuffles the recommendations so they stay fresh and diverse, even if a user’s engagement hasn’t changed. We were finding that some users were receiving, for example, all rain sounds at the top of their recommendations, before adding this shuffle.

After the batch recommendations are processed with these business rules, they’re dropped into Amazon ElastiCache. This allows Calm’s backend to access the recommendations with sub-millisecond latency when the user opens the mobile client.

User testing and results

One major hurdle with recommendation models is that it’s very challenging to evaluate them during offline model development. Amazon Personalize provides offline metrics such as precision, cumulative gain, reciprocal rank, and coverage. These traditional out-of-sample evaluation metrics are limited to measure how well the model predictions performed against a holdout set, not how a user would react to new and relevant recommendations surfaced within a personalized user experience. This is why user online testing is the gold standard for evaluating recommender systems. For more information on online testing, see Using A/B testing to measure the efficacy of recommendations generated by Amazon Personalize.

The first round of user tests we performed was in an email campaign. Compared to integrating with a real-time application, displaying recommendations in email is lower lift. Email templates are quicker to build than application UIs, and don’t have the same scalability issues as pulling recommendations from a web service.

Every weekend, we sent a personalized “Weekly Winddown” email to our users, advertising our new content. For this randomized A/B test, the control group received hand-picked “what’s new” content in their email, whereas the treatment group received recommended “what’s new” content from Amazon Personalize. The treatment group was 1% more likely to complete content in the app within 24 hours of opening the email, a statistically significant result, which concluded that Amazon Personalize recommendations had a positive influence on our user behavior. This email test helped us prove out the recommendation pipeline, and the new automation replaced a previously manual task of choosing new content for our emails.

After building out our in-app architecture, we ran a round of “dogfood” tests, where Calm employees had early access to the “Recommended for You” carousel in the home screen of the mobile clients. We sent around a survey to the early testers, asking them detailed questions about how they responded to the in-app experience. This feedback proved invaluable to identifying silent bugs in our pipeline. For example, our kids-focused content is quite popular with younger users. However, when shown in the recommended carousels of adults, the kids content seemed nonsensical, which can erode user trust.

For our full in-app randomized A/B/C test, we enrolled new users into one of three groups:

  • Control group – They saw no change in the user experience
  • First treatment group – They saw a “Recommended for You” carousel filled with popularity count recommendations
  • Second treatment group – They had their carousel filled with recommendations from Amazon Personalize

This three-pronged test allowed us to disentangle whether any improvement in engagement came from the model specifically, or whether a simpler approach could have achieved similar results. Ultimately, the Amazon Personalize group saw a statistically significant lift of 3.4% over both other groups in our distinct days engagement metric, and this test was one of our most successful in 2020.


For organizations aiming to deliver personalized customer journeys by using ML recommender engines, it can seem daunting when looking at the amount of heavy lifting required to create robust production-level systems. Amazon Personalize provides you with easy-to-use high-performance algorithms plus all the required infrastructure to serve your customers at large scale at a fraction of the cost compared to building it from scratch.

As discussed in this post, Calm has used Amazon Personalize to not only simplify their previously manual tasks, but also to accelerate their development and deployment of high-performance models to provide wellness recommendations to millions of their customers.

If this post helps you or inspires you to use Amazon Personalize to improve your business metrics, please share your thoughts in the comments.

Additional resources

For more information about Amazon Personalize, see the following:

About the Author

Shae Selix is a Staff Data Scientist at Calm, building ML services to promote meditation and sleep habits. He is a daily user of the Calm app and, outside work, is a student and teacher of yoga. As a Northern California native, he loves hiking, biking, and running through the coastlines and mountains in the Bay Area.

Luis Lopez Soria is an AI/ML specialist solutions architect working with the Amazon Machine Learning team. He works with AWS customers to help them adopt machine learning on a large scale. He enjoys playing sports, traveling around the world, and exploring new foods and cultures.


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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API…




In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. Furthermore, we add an additional guardrail to ensure presigned URLs are only generated and accessed for the authenticated end-user within the corporate network.

Solution overview

The following diagram illustrates the architecture of the solution.

The process includes the following steps:

  1. In the Amazon Cognito user pool, first set up a user with the name matching their Studio user profile and register Studio as the app client in the user pool.
  2. The user federates from their corporate identity provider (IdP) and authenticates with the Amazon Cognito user pool for accessing Studio.
  3. Amazon Cognito returns a token to the user authorizing access to the Studio application.
  4. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.
  5. API Gateway invokes a custom AWS Lambda authorizer and validates the token.
  6. When the token is valid, Amazon Cognito returns an access grant policy with studio user profile id to API Gateway.
  7. API Gateway invokes the createStudioPresignedUrl Lambda function for creating the studio presigned url.
  8. The createStudioPresignedUrl function creates a presigned URL using the SageMaker API VPC endpoint and returns to caller.
  9. User accesses the presigned URL from their corporate network that resolves over the Studio VPC endpoint.
  10. The function’s AWS Identity and Access Management (IAM) policy makes sure that the presigned URL creation and access are performed via VPC endpoints.

The following sections walk you through solution deployment, configuration, and validation for the API Gateway private API for creating and resolving a Studio presigned URL from a corporate network using VPC endpoints.

  1. Deploy the solution
  2. Configure the Amazon Cognito user
  3. Authenticating the private API for the presigned URL using a JSON Web Token
  4. Configure the corporate DNS server for accessing the private API
  5. Test the API Gateway private API for a presigned URL from the corporate network
  6. Pre-Signed URL Lambda Auth Policy
  7. Cleanup

Deploy the solution

You can deploy the solution through either the AWS Management Console or the AWS Serverless Application Model (AWS SAM).

To deploy the solution via the console, launch the following AWS CloudFormation template in your account by choosing Launch Stack. It takes approximately 10 minutes for the CloudFormation stack to complete.

To deploy the solution using AWS SAM, you can find the latest code in the aws-samples GitHub repository, where you can also contribute to the sample code. The following commands show how to deploy the solution using the AWS SAM CLI. If not currently installed, install the AWS SAM CLI.

  1. Clone the repository at
  2. After you clone the repo, navigate to the source and run the following code:

Configure the Amazon Cognito user

To configure your Amazon Cognito user, complete the following steps:

  1. Create an Amazon Cognito user with the same name as a SageMaker user profile: aws cognito-idp admin-create-user –user-pool-id –username
  2. Set the user password: aws cognito-idp admin-set-user-password –user-pool-id –username –password –permanent
  3. Get an access token: aws cognito-idp initiate-auth –auth-flow USER_PASSWORD_AUTH –client-id –auth-parameters USERNAME=,PASSWORD=

Authenticating the private API for the presigned URL using a JSON Web Token

When you deployed a private API for creating a SageMaker presigned URL, you added a guardrail to restrict access to access the presigned URL by anyone outside the corporate network and VPC endpoint. However, without implementing another control to the private API within the corporate network, any internal user within the corporate network would be able to pass unauthenticated parameters for the SageMaker user profile and access any SageMaker app.

To mitigate this issue, we propose passing a JSON Web Token (JWT) for the authenticated caller to the API Gateway and validating that token with a JWT authorizer. There are multiple options for implementing an authorizer for the private API Gateway, using either a custom Lambda authorizer or Amazon Cognito.

With a custom Lambda authorizer, you can embed a SageMaker user profile name in the returned policy. This prevents any users within the corporate network from being able to send any SageMaker user profile name for creating a presigned URL that they’re not authorized to create. We use Amazon Cognito to generate our tokens and a custom Lambda authorizer to validate and return the appropriate policy. For more information, refer to Building fine-grained authorization using Amazon Cognito, API Gateway, and IAM. The Lambda authorizer uses the Amazon Cognito user name as the user profile name.

If you’re unable to use Amazon Cognito, you can develop a custom application to authenticate and pass end-user tokens to the Lambda authorizer. For more information, refer to Use API Gateway Lambda authorizers.

Configure the corporate DNS server for accessing the private API

To configure your corporate DNS server, complete the following steps:

  1. On the Amazon Elastic Compute Cloud (Amazon EC2) console, choose your on-premises DNSA EC2 instance and connect via Systems Manager Session Manager.
  2. Add a zone record in the /etc/named.conf file for resolving to the API Gateway’s DNS name via your Amazon Route 53 inbound resolver, as shown in the following code: zone “” { type forward; forward only; forwarders {;; }; };
  3. Restart the named service using the following command: sudo service named restart

Validate requesting a presigned URL from the API Gateway private API for authorized users

In a real-world scenario, you would implement a front-end interface that would pass the appropriate Authorization headers for authenticated and authorized resources using either a custom solution or leverage AWS Amplify. For brevity of this blog post, the following steps leverages Postman to quickly validate the solution we deployed actually restricts requesting the presigned URL for an internal user, unless authorized to do so.

To validate the solution with Postman, complete the following steps:

  1. Install Postman on the WINAPP EC2 instance. See instructions here
  2. Open Postman and add the access token to your Authorization header: Authorization: Bearer
  3. Modify the API Gateway URL to access it from your internal EC2 instance:
    1. Add the VPC endpoint into your API Gateway URL:
    2. Add the Host header with a value of your API Gateway URL:
    3. First, change the EMPLOYEE_ID to your Amazon Cognito user and SageMaker user profile name. Make sure you receive an authorized presigned URL.
    4. Then change the EMPLOYEE_ID to a user that is not yours and make sure you receive an access failure.
  4. On the Amazon EC2 console, choose your on-premises WINAPP instance and connect via your RDP client.
  5. Open a Chrome browser and navigate to your authorized presigned URL to launch Studio.

Studio is launched over VPC endpoint with remote address as the Studio VPC endpoint IP.

If the presigned URL is accessed outside of the corporate network, the resolution fails because the IAM policy condition for the presigned URL enforces creation and access from a VPC endpoint.

Pre-Signed URL Lambda Auth Policy

Above solution created the following Auth Policy for the Lambda that generated Pre-Signed URL for accessing SageMaker Studio.

{ “Version”: “2012-10-17”, “Statement”: [ { “Condition”: { “IpAddress”: { “aws:VpcSourceIp”: “” } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” }, { “Condition”: { “IpAddress”: { “aws:SourceIp”: “” } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” }, { “Condition”: { “StringEquals”: { “aws:sourceVpce”: [ “vpce-sm-api-xx”, “vpce-sm-api-yy” ] } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” } ] }

The above policy enforces Studio pre-signed URL is both generated and accessed via one of these three entrypoints:

  1. aws:VpcSourceIp as your AWS VPC CIDR
  2. aws:SourceIp as your corporate network CIDR
  3. aws:sourceVpce as your SageMaker API VPC endpoints


To avoid incurring ongoing charges, delete the CloudFormation stacks you created. Alternatively, if you deployed the solution using SAM, you need to authenticate to the AWS account the solution was deployed and run sam delete.


In this post, we demonstrated how to access Studio using a private API Gateway from a corporate network using Amazon private VPC endpoints, preventing access to presigned URLs outside the corporate network, and securing the API Gateway with a JWT authorizer using Amazon Cognito and custom Lambda authorizers.

Try out with this solution and experiment integrating this with your corporate portal, and leave your feedback in the comments!

About the Authors

Ram Vittal is a machine learning solutions architect at AWS. He has over 20+ years of experience architecting and building distributed, hybrid and cloud applications. He is passionate about building secure and scalable AI/ML and Big Data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he enjoys tennis, photography, and action movies.

Jonathan Nguyen is a Shared Delivery Team Senior Security Consultant at AWS. His background is in AWS Security with a focus on Threat Detection and Incident Response. Today, he helps enterprise customers develop a comprehensive AWS Security strategy, deploy security solutions at scale, and train customers on AWS Security best practices.

Chris Childers is a Cloud Infrastructure Architect in Professional Services at AWS. He works with AWS customers to design and automate their cloud infrastructure and improve their adoption of DevOps culture and processes.


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Secure Amazon SageMaker Studio presigned URLs Part 1: Foundational infrastructure

You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for…




You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for the user. Because the SageMaker console runs on an internet domain, this generated presigned URL is visible in the browser session. This presents an undesired threat vector for exfiltration and gaining access to customer data when proper access controls are not enforced.

Studio supports a few methods for enforcing access controls against presigned URL data exfiltration:

  • Client IP validation using the IAM policy condition aws:sourceIp
  • Client VPC validation using the IAM condition aws:sourceVpc
  • Client VPC endpoint validation using the IAM policy condition aws:sourceVpce

When you access Studio notebooks from the SageMaker console, the only available option is to use client IP validation with the IAM policy condition aws:sourceIp. However, you can use browser traffic routing products such as Zscaler to ensure scale and compliance for your workforce internet access. These traffic routing products generate their own source IP, whose IP range is not controlled by the enterprise customer. This makes it impossible for these enterprise customers to use the aws:sourceIp condition.

To use client VPC endpoint validation using the IAM policy condition aws:sourceVpce, the creation of a presigned URL needs to originate in the same customer VPC where Studio is deployed, and resolution of the presigned URL needs to happen via a Studio VPC endpoint on the customer VPC. This resolution of the presigned URL during access time for corporate network users can be accomplished using DNS forwarding rules (both in Zscaler and corporate DNS) and then into the customer VPC endpoint using an AWS Route 53 inbound resolver.

In this part, we discuss the overarching architecture for securing studio pre-signed url and demonstrate how to set up the foundational infrastructure to create and launch a Studio presigned URL through your VPC endpoint over a private network without traversing the internet. This serves as the foundational layer for preventing data exfiltration by external bad actors gaining access to Studio pre-signed URL and unauthorized or spoofed corporate user access within a corporate environment.

Solution overview

The following diagram illustrates over-arching solution architecture.

The process includes the following steps:

  1. A corporate user authenticates via their IdP, connects to their corporate portal, and opens the Studio link from the corporate portal.
  2. The corporate portal application makes a private API call using an API Gateway VPC endpoint to create a presigned URL.
  3. The API Gateway VPC endpoint “create presigned URL” call is forwarded to the Route 53 inbound resolver on the customer VPC as configured in the corporate DNS.
  4. The VPC DNS resolver resolves it to the API Gateway VPC endpoint IP. Optionally, it looks up a private hosted zone record if it exists.
  5. The API Gateway VPC endpoint routes the request via the Amazon private network to the “create presigned URL API” running in the API Gateway service account.
  6. API Gateway invokes the create-pre-signedURL private API and proxies the request to the create-pre-signedURL Lambda function.
  7. The create-pre-signedURL Lambda call is invoked via the Lambda VPC endpoint.
  8. The create-pre-signedURL function runs in the service account, retrieves authenticated user context (user ID, Region, and so on), looks up a mapping table to identify the SageMaker domain and user profile identifier, makes a sagemaker createpre-signedDomainURL API call, and generates a presigned URL. The Lambda service role has the source VPC endpoint conditions defined for the SageMaker API and Studio.
  9. The generated presigned URL is resolved over the Studio VPC endpoint.
  10. Studio validates that the presigned URL is being accessed via the customer’s VPC endpoint defined in the policy, and returns the result.
  11. The Studio notebook is returned to the user’s browser session over the corporate network without traversing the internet.

The following sections walk you through how to implement this architecture to resolve Studio presigned URLs from a corporate network using VPC endpoints. We demonstrate a complete implementation by showing the following steps:

  1. Set up the foundational architecture.
  2. Configure the corporate app server to access a SageMaker presigned URL via a VPC endpoint.
  3. Set up and launch Studio from the corporate network.

Set up the foundational architecture

In the post Access an Amazon SageMaker Studio notebook from a corporate network, we demonstrated how to resolve a presigned URL domain name for a Studio notebook from a corporate network without traversing the internet. You can follow the instructions in that post to set up the foundational architecture, and then return to this post and proceed to the next step.

Configure the corporate app server to access a SageMaker presigned URL via a VPC endpoint

To enable accessing Studio from your internet browser, we set up an on-premises app server on Windows Server on the on-premises VPC public subnet. However, the DNS queries for accessing Studio are routed through the corporate (private) network. Complete the following steps to configure routing Studio traffic through the corporate network:

  1. Connect to your on-premises Windows app server.

  2. Choose Get Password then browse and upload your private key to decrypt your password.
  3. Use an RDP client and connect to the Windows Server using your credentials.
    Resolving Studio DNS from the Windows Server command prompt results in using public DNS servers, as shown in the following screenshot.
    Now we update Windows Server to use the on-premises DNS server that we set up earlier.
  4. Navigate to Control Panel, Network and Internet, and choose Network Connections.
  5. Right-click Ethernet and choose the Properties tab.
  6. Update Windows Server to use the on-premises DNS server.
  7. Now you update your preferred DNS server with your DNS server IP.
  8. Navigate to VPC and Route Tables and choose your STUDIO-ONPREM-PUBLIC-RT route table.
  9. Add a route to with the target as the peering connection that we created during the foundational architecture setup.

Set up and launch Studio from your corporate network

To set up and launch Studio, complete the following steps:

  1. Download Chrome and launch the browser on this Windows instance.
    You may need to turn off Internet Explorer Enhanced Security Configuration to allow file downloads and then enable file downloads.
  2. In your local device Chrome browser, navigate to the SageMaker console and open the Chrome developer tools Network tab.
  3. Launch the Studio app and observe the Network tab for the authtokenparameter value, which includes the generated presigned URL along with the remote server address that the URL is routed to for resolution.In this example, the remote address is one of the public DNS server addresses to resolve the SageMaker DNS domain name
  4. Repeat these steps from the Amazon Elastic Compute Cloud (Amazon EC2) Windows instance that you configured as part of the foundational architecture.

We can observe that the remote address is not the public DNS IP, instead it’s the Studio VPC endpoint


In this post, we demonstrated how to resolve a Studio presigned URL from a corporate network using Amazon private VPC endpoints without exposing the presigned URL resolution to the internet. This further secures your enterprise security posture for accessing Studio from a corporate network for building highly secure machine learning workloads on SageMaker. In part 2 of this series, we further extend this solution to demonstrate how to build a private API for accessing Studio with aws:sourceVPCE IAM policy validation and token authentication. Try out this solution and leave your feedback in the comments!

About the Authors

Ram Vittal is a machine learning solutions architect at AWS. He has over 20+ years of experience architecting and building distributed, hybrid and cloud applications. He is passionate about building secure and scalable AI/ML and Big Data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he enjoys tennis and photography.

Neelam Koshiya is an enterprise solution architect at AWS. Her current focus is to help enterprise customers with their cloud adoption journey for strategic business outcomes. In her spare time, she enjoys reading and being outdoors.


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Use a custom image to bring your own development environment to RStudio on Amazon SageMaker

RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on…




RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on SageMaker already comes with a built-in image preconfigured with R programming and data science tools; however, you often need to customize your IDE environment. Starting today, you can bring your own custom image with packages and tools of your choice, and make them available to all the users of RStudio on SageMaker in a few clicks.

Bringing your own custom image has several benefits. You can standardize and simplify the getting started experience for data scientists and developers by providing a starter image, preconfigure the drivers required for connecting to data stores, or pre-install specialized data science software for your business domain. Furthermore, organizations that have previously hosted their own RStudio Workbench may have existing containerized environments that they want to continue to use in RStudio on SageMaker.

In this post, we share step-by-step instructions to create a custom image and bring it to RStudio on SageMaker using the AWS Management Console or AWS Command Line Interface (AWS CLI). You can get your first custom IDE environment up and running in few simple steps. For more information on the content discussed in this post, refer to Bring your own RStudio image.

Solution overview

When a data scientist starts a new session in RStudio on SageMaker, a new on-demand ML compute instance is provisioned and a container image that defines the runtime environment (operating system, libraries, R versions, and so on) is run on the ML instance. You can provide your data scientists multiple choices for the runtime environment by creating custom container images and making them available on the RStudio Workbench launcher, as shown in the following screenshot.

The following diagram describes the process to bring your custom image. First you build a custom container image from a Dockerfile and push it to a repository in Amazon Elastic Container Registry (Amazon ECR). Next, you create a SageMaker image that points to the container image in Amazon ECR, and attach that image to your SageMaker domain. This makes the custom image available for launching a new session in RStudio.


To implement this solution, you must have the following prerequisites:

We provide more details on each in this section.

RStudio on SageMaker domain

If you have an existing SageMaker domain with RStudio enabled prior to April 7, 2022, you must delete and recreate the RStudioServerPro app under the user profile name domain-shared to get the latest updates for bring your own custom image capability. The AWS CLI commands are as follows. Note that this action interrupts RStudio users on SageMaker.

aws sagemaker delete-app –domain-id –app-type RStudioServerPro –app-name default –user-profile-name domain-shared aws sagemaker create-app –domain-id –app-type RStudioServerPro –app-name default –user-profile-name domain-shared

If this is your first time using RStudio on SageMaker, follow the step-by-step setup process described in Get started with RStudio on Amazon SageMaker, or run the following AWS CloudFormation template to set up your first RStudio on SageMaker domain. If you already have a working RStudio on SageMaker domain, you can skip this step.

The following RStudio on SageMaker CloudFormation template requires an RStudio license approved through AWS License Manager. For more about licensing, refer to RStudio license. Also note that only one SageMaker domain is permitted per AWS Region, so you’ll need to use an AWS account and Region that doesn’t have an existing domain.

  1. Choose Launch Stack.
    Launch stack button
    The link takes you to the us-east-1 Region, but you can change to your preferred Region.
  2. In the Specify template section, choose Next.
  3. In the Specify stack details section, for Stack name, enter a name.
  4. For Parameters, enter a SageMaker user profile name.
  5. Choose Next.
  6. In the Configure stack options section, choose Next.
  7. In the Review section, select I acknowledge that AWS CloudFormation might create IAM resources and choose Next.
  8. When the stack status changes to CREATE_COMPLETE, go to the Control Panel on the SageMaker console to find the domain and the new user.

IAM policies to interact with Amazon ECR

To interact with your private Amazon ECR repositories, you need the following IAM permissions in the IAM user or role you’ll use to build and push Docker images:

{ “Version”:”2012-10-17″, “Statement”:[ { “Sid”: “VisualEditor0”, “Effect”:”Allow”, “Action”:[ “ecr:CreateRepository”, “ecr:BatchGetImage”, “ecr:CompleteLayerUpload”, “ecr:DescribeImages”, “ecr:DescribeRepositories”, “ecr:UploadLayerPart”, “ecr:ListImages”, “ecr:InitiateLayerUpload”, “ecr:BatchCheckLayerAvailability”, “ecr:PutImage” ], “Resource”: “*” } ] }

To initially build from a public Amazon ECR image as shown in this post, you need to attach the AWS-managed AmazonElasticContainerRegistryPublicReadOnly policy to your IAM user or role as well.

To build a Docker container image, you can use either a local Docker client or the SageMaker Docker Build CLI tool from a terminal within RStudio on SageMaker. For the latter, follow the prerequisites in Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks to set up the IAM permissions and CLI tool.

AWS CLI versions

There are minimum version requirements for the AWS CLI tool to run the commands mentioned in this post. Make sure to upgrade AWS CLI on your terminal of choice:

  • AWS CLI v1 >= 1.23.6
  • AWS CLI v2 >= 2.6.2

Prepare a Dockerfile

You can customize your runtime environment in RStudio in a Dockerfile. Because the customization depends on your use case and requirements, we show you the essentials and the most common customizations in this example. You can download the full sample Dockerfile.

Install RStudio Workbench session components

The most important software to install in your custom container image is RStudio Workbench. We download from the public S3 bucket hosted by RStudio PBC. There are many version releases and OS distributions for use. The version of the installation needs to be compatible with the RStudio Workbench version used in RStudio on SageMaker, which is 1.4.1717-3 at the time of writing. The OS (argument OS in the following snippet) needs to match the base OS used in the container image. In our sample Dockerfile, the base image we use is Amazon Linux 2 from an AWS-managed public Amazon ECR repository. The compatible RStudio Workbench OS is centos7.

FROM … ARG RSW_VERSION=1.4.1717-3 ARG RSW_NAME=rstudio-workbench-rhel ARG OS=centos7 ARG RSW_DOWNLOAD_URL=${OS}/x86_64 RUN RSW_VERSION_URL=`echo -n “${RSW_VERSION}” | sed ‘s/+/-/g’` && curl -o rstudio-workbench.rpm ${RSW_DOWNLOAD_URL}/${RSW_NAME}-${RSW_VERSION_URL}-x86_64.rpm && yum install -y rstudio-workbench.rpm

You can find all the OS release options with the following command:

aws s3 ls s3://rstudio-ide-build/server/

Install R (and versions of R)

The runtime for your custom RStudio container image needs at least one version of R. We can first install a version of R and make it the default R by creating soft links to /usr/local/bin/:

# Install main R version ARG R_VERSION=4.1.3 RUN curl -O${R_VERSION}-1-1.x86_64.rpm && yum install -y R-${R_VERSION}-1-1.x86_64.rpm && yum clean all && rm -rf R-${R_VERSION}-1-1.x86_64.rpm RUN ln -s /opt/R/${R_VERSION}/bin/R /usr/local/bin/R && ln -s /opt/R/${R_VERSION}/bin/Rscript /usr/local/bin/Rscript

Data scientists often need multiple versions of R so that they can easily switch between projects and code base. RStudio on SageMaker supports easy switching between R versions, as shown in the following screenshot.

RStudio on SageMaker automatically scans and discovers versions of R in the following directories:

/usr/lib/R /usr/lib64/R /usr/local/lib/R /usr/local/lib64/R /opt/local/lib/R /opt/local/lib64/R /opt/R/* /opt/local/R/*

We can install more versions in the container image, as shown in the following snippet. They will be installed in /opt/R/.

RUN curl -O && yum install -y R-4.0.5-1-1.x86_64.rpm && yum clean all && rm -rf R-4.0.5-1-1.x86_64.rpm RUN curl -O && yum install -y R-3.6.3-1-1.x86_64.rpm && yum clean all && rm -rf R-3.6.3-1-1.x86_64.rpm RUN curl -O && yum install -y R-3.5.3-1-1.x86_64.rpm && yum clean all && rm -rf R-3.5.3-1-1.x86_64.rpm

Install RStudio Professional Drivers

Data scientists often need to access data from sources such as Amazon Athena and Amazon Redshift within RStudio on SageMaker. You can do so using RStudio Professional Drivers and RStudio Connections. Make sure you install the relevant libraries and drivers as shown in the following snippet:

# Install RStudio Professional Drivers —————————————-# RUN yum update -y && yum install -y unixODBC unixODBC-devel && yum clean all ARG DRIVERS_VERSION=2021.10.0-1 RUN curl -O${DRIVERS_VERSION}.el7.x86_64.rpm && yum install -y rstudio-drivers-${DRIVERS_VERSION}.el7.x86_64.rpm && yum clean all && rm -f rstudio-drivers-${DRIVERS_VERSION}.el7.x86_64.rpm && cp /opt/rstudio-drivers/odbcinst.ini.sample /etc/odbcinst.ini RUN /opt/R/${R_VERSION}/bin/R -e ‘install.packages(“odbc”, repos=””)’

Install custom libraries

You can also install additional R and Python libraries so that data scientists don’t need to install them on the fly:

RUN /opt/R/${R_VERSION}/bin/R -e “install.packages(c(‘reticulate’, ‘readr’, ‘curl’, ‘ggplot2’, ‘dplyr’, ‘stringr’, ‘fable’, ‘tsibble’, ‘dplyr’, ‘feasts’, ‘remotes’, ‘urca’, ‘sodium’, ‘plumber’, ‘jsonlite’), repos=’’)” RUN /opt/python/${PYTHON_VERSION}/bin/pip install –upgrade ‘boto3>1.0<2.0' 'awscli>1.0<2.0' 'sagemaker[local]<3' 'sagemaker-studio-image-build' 'numpy'

When you’ve finished your customization in a Dockerfile, it’s time to build a container image and push it to Amazon ECR.

Build and push to Amazon ECR

You can build a container image from the Dockerfile from a terminal where the Docker engine is installed, such as your local terminal or AWS Cloud9. If you’re building it from a terminal within RStudio on SageMaker, you can use SageMaker Studio Image Build. We demonstrate the steps for both approaches.

In a local terminal where the Docker engine is present, you can run the following commands from where the Dockerfile is. You can use the sample script

IMAGE_NAME=r-4.1.3-rstudio-1.4.1717-3 # the name for SageMaker Image REPO=rstudio-custom # ECR repository name TAG=$IMAGE_NAME # login to your Amazon ECR aws ecr get-login-password | docker login –username AWS –password-stdin ${ACCOUNT_ID}.dkr.ecr.${REGION} # create a repo aws ecr create-repository –repository-name ${REPO} # build a docker image and push it to the repo docker build . -t ${REPO}:${TAG} -t ${ACCOUNT_ID}.dkr.ecr.${REGION}${REPO}:${TAG} docker push ${ACCOUNT_ID}.dkr.ecr.${REGION}${REPO}:${TAG}

In a terminal on RStudio on SageMaker, run the following commands:

pip install sagemaker-studio-image-build sm-docker build . –repository ${REPO}:${IMAGE_NAME}

After these commands, you have a repository and a Docker container image in Amazon ECR for our next step, in which we attach the container image for use in RStudio on SageMaker. Note the image URI in Amazon ECR for later use.

Update RStudio on SageMaker through the console

RStudio on SageMaker allows runtime customization through the use of a custom SageMaker image. A SageMaker image is a holder for a set of SageMaker image versions. Each image version represents a container image that is compatible with RStudio on SageMaker and stored in an Amazon ECR repository. To make a custom SageMaker image available to all RStudio users within a domain, you can attach the image to the domain following the steps in this section.

  1. On the SageMaker console, navigate to the Custom SageMaker Studio images attached to domain page, and choose Attach image.
  2. Select New image, and enter your Amazon ECR image URI.
  3. Choose Next.
  4. In the Image properties section, provide an Image name (required), Image display name (optional), Description (optional), IAM role, and tags.
    The image display name, if provided, is shown in the session launcher in RStudio on SageMaker. If the Image display name field is left empty, the image name is shown in RStudio on SageMaker instead.
  5. Leave EFS mount path and Advanced configuration (User ID and Group ID) as default because RStudio on SageMaker manages the configuration for us.
  6. In the Image type section, select RStudio image.
  7. Choose Submit.

You can now see a new entry in the list. It’s worth noting that, with the introduction of the support of custom RStudio images, you can see a new Usage type column in the table to denote whether an image is an RStudio image or an Amazon SageMaker Studio image.

It may take up to 5–10 minutes for the custom images to be available in the session launcher UI. You can then launch a new R session in RStudio on SageMaker with your custom images.

Over time, you may want to retire old and outdated images. To remove the custom images from the list of custom images in RStudio, select the images in the list and choose Detach.

Choose Detach again to confirm.

Update RStudio on SageMaker via the AWS CLI

The following sections describe the steps to create a SageMaker image and attach it for use in RStudio on SageMaker on the SageMaker console and using the AWS CLI. You can use the sample script

Create the SageMaker image and image version

The first step is to create a SageMaker image from the custom container image in Amazon ECR by running the following two commands:

ROLE_ARN= DISPLAY_NAME=RSession-r-4.1.3-rstudio-1.4.1717-3 aws sagemaker create-image –image-name ${IMAGE_NAME} –display-name ${DISPLAY_NAME} –role-arn ${ROLE_ARN} aws sagemaker create-image-version –image-name ${IMAGE_NAME} –base-image “${ACCOUNT_ID}.dkr.ecr.${REGION}${REPO}:${TAG}”

Note that the custom image displayed in the session launcher in RStudio on SageMaker is determined by the input of –display-name. If the optional display name is not provided, the input of –image-name is used instead. Also note that the IAM role allows SageMaker to attach an Amazon ECR image to RStudio on SageMaker.

Create an AppImageConfig

In addition to a SageMaker image, which captures the image URI from Amazon ECR, an app image configuration (AppImageConfig) is required for use in a SageMaker domain. We simplify the configuration for an RSessionApp image so we can just create a placeholder configuration with the following command:

IMAGE_CONFIG_NAME=r-4-1-3-rstudio-1-4-1717-3 aws sagemaker create-app-image-config –app-image-config-name ${IMAGE_CONFIG_NAME}

Attach to a SageMaker domain

With the SageMaker image and the app image configuration created, we’re ready to attach the custom container image to the SageMaker domain. To make a custom SageMaker image available to all RStudio users within a domain, you attach the image to the domain as a default user setting. All existing users and any new users will be able to use the custom image.

For better readability, we place the following configuration into the JSON file default-user-settings.json:

“DefaultUserSettings”: { “RSessionAppSettings”: { “CustomImages”: [ { “ImageName”: “r-4.1.3-rstudio-2022”, “AppImageConfigName”: “r-4-1-3-rstudio-2022” }, { “ImageName”: “r-4.1.3-rstudio-1.4.1717-3”, “AppImageConfigName”: “r-4-1-3-rstudio-1-4-1717-3” } ] } } }

In this file, we can specify the image and AppImageConfig name pairs in a list in DefaultUserSettings.RSessionAppSettings.CustomImages. This preceding snippet assumes two custom images are being created.

Then run the following command to update the SageMaker domain:

aws sagemaker update-domain –domain-id –cli-input-json file://default-user-settings.json

After you update the domaim, it may take up to 5–10 minutes for the custom images to be available in the session launcher UI. You can then launch a new R session in RStudio on SageMaker with your custom images.

Detach images from a SageMaker domain

You can detach images simply by removing the ImageName and AppImageConfigName pairs from default-user-settings.json and updating the domain.

For example, updating the domain with the following default-user-settings.json removes r-4.1.3-rstudio-2022 from the R session launching UI and leaves r-4.1.3-rstudio-1.4.1717-3 as the only custom image available to all users in a domain:

{ “DefaultUserSettings”: { “RSessionAppSettings”: { “CustomImages”: [ { “ImageName”: “r-4.1.3-rstudio-1.4.1717-3”, “AppImageConfigName”: “r-4-1-3-rstudio-1-4-1717-3” } ] } } }

Clean up

To safely remove images and resources in the SageMaker domain, complete the following steps in Clean up image resources.

To safely remove the RStudio on SageMaker and the SageMaker domain, complete the following steps in Delete an Amazon SageMaker Domain to delete any RSessionGateway app, user profile and the domain.

To safely remove images and repositories in Amazon ECR, complete the following steps in Deleting an image.

Finally, to delete the CloudFormation template:

  1. On the AWS CloudFormation console, choose Stacks.
  2. Select the stack you deployed for this solution.
  3. Choose Delete.


RStudio on SageMaker makes it simple for data scientists to build ML and analytic solutions in R at scale, and for administrators to manage a robust data science environment for their developers. Data scientists want to customize the environment so that they can use the right libraries for the right job and achieve the desired reproducibility for each ML project. Administrators need to standardize the data science environment for regulatory and security reasons. You can now create custom container images that meet your organizational requirements and allow data scientists to use them in RStudio on SageMaker.

We encourage you to try it out. Happy developing!

About the Authors

Michael Hsieh is a Senior AI/ML Specialist Solutions Architect. He works with customers to advance their ML journey with a combination of AWS ML offerings and his ML domain knowledge. As a Seattle transplant, he loves exploring the great Mother Nature the city has to offer, such as the hiking trails, scenery kayaking in the SLU, and the sunset at Shilshole Bay.

Declan Kelly is a Software Engineer on the Amazon SageMaker Studio team. He has been working on Amazon SageMaker Studio since its launch at AWS re:Invent 2019. Outside of work, he enjoys hiking and climbing.

Sean MorganSean Morgan is an AI/ML Solutions Architect at AWS. He has experience in the semiconductor and academic research fields, and uses his experience to help customers reach their goals on AWS. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Add-ons.


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