As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. These organizations are embracing the cloud to achieve various goals, including to:
While RStudio provides many different ways to support an organization’s cloud strategy, we’ve heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio’s professional products with SageMaker’s rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker.
Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team.
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.
RStudio is excited to collaborate with the Amazon SageMaker team on this release as they make it easier for organizations to move their open-source data science workloads to the cloud. We are committed to helping our joint customers use our commercial offerings to bring their production workloads to Amazon’s SageMaker, and to further collaborations with the Amazon SageMaker team.
— Tareef Kawaf, President, RStudio PBC
Data scientists can quickly get to work, spinning up their favorite development environment on SageMaker. They can:
Within that environment, they can get access to their organization’s data stored on AWS. They also have access to all of SageMaker’s deep learning capabilities, accessed via Python libraries using the reticulate package. This preconfigured environment includes all the necessary SageMaker libraries to get started.
This offering complements Amazon SageMaker Studio Notebooks, which provide access to Python coding in a Jupyter Notebook environment. This means that data scientists proficient with both R and Python can freely switch between RStudio and SageMaker Studio Notebooks. All of their work, including code, datasets, repositories, and other artifacts are synchronized between the two environments through the default Amazon Elastic File System (Amazon EFS) storage.
For more information from the data scientist perspective, see Announcing Fully Managed RStudio on Amazon SageMaker for Data Scientists.
As a fully managed offering on Amazon SageMaker, this release makes it easy for DevOps teams and IT Admins to administer, secure and scale their organization’s centralized data science infrastructure. They can:
For more information from the DevOps perspective, see Getting Started with RStudio on Amazon SageMaker.
For data-driven organizations already using AWS, this provides a way to migrate their self-managed RStudio environments to AWS SageMaker, using their existing RStudio Workbench licenses without an incremental cost.
When RStudio for SageMaker is configured for use with RStudio Connect, data scientists using both RStudio for SageMaker and SageMaker Studio can easily share their R and Python insights with their decision-makers.
RStudio Connect makes it easy to deliver key insights to decision-makers, at the right time, in the right format. Connect supports a spectrum of data products, static or dynamic, developed in R and Python: Dashboards, applications, APIs, reports, and more.
For more information, see Host RStudio Connect and Package Manager for ML Development in RStudio on Amazon SageMaker.
RStudio for Amazon SageMaker enables RStudio Workbench customers to bring their existing licenses to SageMaker. If you are an existing customer, or would like to learn more, please reach out to your customer success manager or schedule a time to talk with us.
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