Run Positron on Amazon SageMaker AI for data science workflows

Posit's Positron IDE now runs on Amazon SageMaker AI, enabling data scientists to explore data, train models in R and Python, deploy endpoints, and report results in one governed environment.

By AIWI EditorialReviewed by Chayanika DivekarAI-assisted summary · human-reviewed1 min readPublished Sep 21, 2026

Single source1 source

Reality Check

Availability: Shipping now
Hype-o-meter: Mostly substance
Who it's for: Data scientists · Enterprises
Do this now: Review the AWS post to set up Positron within a SageMaker Studio Space for R and Python workflows.
Claims check: Claims describe a demonstrated end-to-end workflow; no performance metrics are provided in the input.
Corroboration: Single source · 1 source

Key Takeaways

Positron, Posit's data science IDE, now runs on Amazon SageMaker AI
Workflow covers Athena exploration, R feature validation, and Python XGBoost training
Supports deploying a real-time SageMaker AI endpoint and reporting with Quarto
Runs within a single governed SageMaker Studio Space

Positron, the data science IDE from Posit, is now available to run on Amazon SageMaker AI. AWS describes an end-to-end workflow within a single governed SageMaker Studio Space: a data scientist explores an Amazon Athena table, validates features in R, trains an XGBoost model in Python, deploys a real-time SageMaker AI endpoint, and generates a report using Quarto.

Why it matters

The integration brings together R and Python tooling in one environment, allowing multiple stages of a data science project — from exploration to model deployment and reporting — to happen inside a governed workspace.

Who should care

Data scientists and teams already using SageMaker AI or Posit’s tools may find this relevant, particularly those working across both R and Python and needing a managed, governed setup.

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Sources & Further Reading

Run Positron on Amazon SageMaker AI for data science workflows — AWS Machine Learning Blog · Sep 21, 2026

Frequently Asked Questions

What languages does the described workflow use?

The workflow uses R to validate features and Python to train an XGBoost model.

How are results reported in the workflow?

Results are reported using Quarto.

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