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Run Positron on Amazon SageMaker AI for data science workflows

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Run Positron on Amazon SageMaker AI for data science workflows

Data science teams often move among separate tools for governed data access, R analysis, Python model development, deployment, application development, and reporting. Positron, Posit’s integrated development environment (IDE) for data science, now runs on Amazon SageMaker AI.

Posit publishes a container image definition for Positron, built on the Amazon SageMaker Distribution image. Platform administrators build that image, push it to their own Amazon Elastic Container Registry (Amazon ECR) repository, register it with SageMaker AI, and attach it to a Studio domain. Data scientists then choose Positron when they create a Space and open the IDE directly in Studio.

This post shows how a data scientist experiences Positron in SageMaker AI, from exploring an Amazon Athena table to deploying a real-time endpoint.

This walkthrough uses a synthetic 50,000-loan portfolio. Amazon S3 stores the source data, and the AWS Glue Data Catalog registers it. Amazon Athena queries the data, R validates features, and Python trains an XGBoost classifier. Shiny for Python invokes the endpoint, and Quarto records the workflow. The screenshots and metrics come from the captured run. The data does not represent a production lending system.

Figure 2. One project connects governed AWS data, R and Python analysis, managed serving, an application, and a reproducible report.

The run began with Positron in a SageMaker Studio Space. The project explorer, editor, R and Python sessions, Variables pane, plots, terminal, and application preview were available in one browser-based environment on SageMaker compute under the Space execution role.

Figure 3. A JupyterLab Space configured to run the Positron custom image.


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