Released in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, creation and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we are excited to announce the enhanced SageMaker Studio experience, which allows users to choose the managed integrated development environment (IDE) of their choice while accessing the resources and tools of SageMaker Studio in the IDE. This updated user experience (UX) gives data scientists, data engineers, and ML engineers more choices about where to build and train their ML models in SageMaker Studio. As a web application, SageMaker Studio has improved load time, faster IDE and kernel startup times, and automatic upgrades.
In addition to managed JupyterLab and RStudio on Amazon SageMaker, we’ve also released managed Open Source Visual Studio Code (Code-OSS) with SageMaker Studio. Once a user selects CodeWhisperer and launches the CodeWhisperer space supported by the compute and storage of their choice, they can take advantage of SageMaker tools and the 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 in SageMaker, users can change the Code Editor calculation on the fly based on their needs.
Finally, to simplify the data science process and avoid users jumping from the console to Amazon SageMaker Studio, we added the ability to view Training Jobs and Endpoints to the SageMaker Studio user interface (UI) and enabled the ability to view all instances which are executed in all launched applications. Additionally, we’ve improved our experience with Jumpstart (FM) base models so users can quickly discover, import, register, tune, and deploy an FM.
Solution overview
Launch IDE
With the new release 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 which 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 user interface enables you to access and discover SageMaker ML resources and tools 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 the Code Editor.
You also have the option to create a new space in SageMaker Studio Classic, which will be shared with all users in your domain.
Improved ML workflow
With the new interactive experience, there are significant improvements and simplification of parts of the existing ML workflow from Amazon SageMaker. In particular, in Training and Hosting there is a much more intuitive UI-based experience for creating new jobs and endpoints, while providing metric monitoring and tracking interfaces.
Education
For training models in Amazon SageMaker, users can perform different flavors of training either through a Studio Notebook via a notebook job, a custom training job, or an optimization job through SageMaker JumpStart. With the improved user interface experience, you can track past and current training jobs using the Studio Training panel.
You can also switch between specific training jobs to understand performance, location of modeling artifacts, and configurations such as hardware and hyperparameters behind a training job. The user interface also provides the flexibility to be able to start and stop training jobs through the Console.
Hospitality
There are also a variety of different hosting options in Amazon SageMaker that you can use to develop models within the UI. To create a SageMaker Endpoint, you can go to Models section where you can use existing models or create a new one.
Here you can use either a single model to deploy an Amazon SageMaker real-time endpoint or multiple models to work with Advanced SageMaker hosting options.
Optionally for FMs, you can also use the Amazon SageMaker JumpStart panel to toggle between the list of available FMs and either optimize or deploy through the UI.
Regulation
The updated Amazon SageMaker Studio experience is released alongside the Amazon SageMaker Studio Classic experience. You can test the new user interface 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 are now able to select their preferred compute-supported IDE of their choice and start the core in seconds, with access to SageMaker tools and resources through the web application SageMaker Studio. The addition of Training and Endpoint details in SageMaker Studio, as well as the improved Amazon SageMaker Jumpstart UX, provides a seamless integration of ML steps into the SageMaker Studio UX. Get started in SageMaker Studio here.
About the Authors
Mair Hasco is an AI/ML expert for Amazon SageMaker Studio. Helps customers optimize their machine learning workloads using Amazon SageMaker.
Ram Vegiraju is an ML architect with the SageMaker Service team. He focuses on helping customers build and optimize their AI/ML solutions in Amazon SageMaker. In his free time he loves traveling and writing.
Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. He has a decade of experience in DevOps, infrastructure and ML. He is also the author of a book on computer vision. In her free 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 enjoys playing with her 1-year-old daughter.