Amazon SageMaker Studio

Amazon SageMaker Studio features

Perform end-to-end AI model development with a fully managed IDE

JupyterLab

Launch fully managed JupyterLab in seconds. Use the latest web-based interactive development environment for notebooks, code, and data. Its flexible and extensible interface allows you to easily configure AI and ML workflows. Get AI-powered assistance for code generation, troubleshooting, and expert guidance to accelerate your AI model development—all within your notebook environment.

Code Editor, based on Code-OSS

Use the lightweight and powerful code editor, and boost productivity with its familiar shortcuts, terminal, debugger, and refactoring tools. Choose from thousands of Visual Studio Code–compatible extensions available in the Open VSX extension gallery to enhance your development experience. Enable versioning control and cross-team collaboration through GitHub repositories. Use the most popular frameworks out of the box with the preconfigured SageMaker AI distribution. Seamlessly integrate with AWS services through the AWS Toolkit for Visual Studio Code, including built-in access to AWS data sources such as Amazon Simple Storage Service (Amazon S3) and Amazon Redshift, and increase coding efficiency through chat-based and inline code suggestions powered by Amazon Q Developer.

RStudio

Use the fully managed integrated development environment (IDE) for R with a console, a syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging, and workspace management. Use preconfigured R packages such as devtools, tidyverse, shiny, and rmarkdown to generate insights, and publish them using RStudio Connect. You can seamlessly switch within RStudio, JupyterLab, and Code Editor IDEs for R and Python development.

Build AI models with Visual Studio Code

Connect from Visual Studio Code to Amazon SageMaker Studio development environments in minutes to rapidly scale your model development. Use your local VS Code setup, including AI-assisted development tools and custom extensions, while accessing SageMaker AI scalable compute resources. Authenticate using the AWS Toolkit extension in VS Code or through the SageMaker Studio web interface. Then connect to any SageMaker Studio development environment in a few steps. Maintain the same security boundaries as SageMaker Studio web-based environments while developing AI models and analyzing data in Visual Studio Code.

Learn more about building AI models with your Visual Studio Code

Screenshot of the Amazon SageMaker Studio Space local IDE interface, showing workspace management, application launch options (JupyterLab, RStudio, Canvas, Code Editor, MLflow), space settings, and remote access controls in a machine learning development environment.

Access and evaluate FMs

Quickly get started with generative AI development using hundreds of publicly available FMs and prebuilt solutions that can be deployed in just a few steps from Amazon SageMaker JumpStart. Quickly evaluate, compare, and select the best FMs for your use case based on a variety of criteria, such as accuracy, robustness, toxicity, and bias within minutes, using Amazon SageMaker Clarify. Get started with FM evaluations by using curated prompt datasets or extend the evaluation with your own custom prompt datasets. Human evaluations can be used for more subjective dimensions such as creativity and style.

Screenshot of the Model Evaluations dashboard in Amazon SageMaker Studio, showing evaluation results for large language models (LLMs) including job names, statuses, model names, evaluation types, and sample notebook options.

Prepare data at scale

Simplify your data workflows with a unified environment for data engineering, analytics, ML and AI model development. Run Spark jobs interactively using Amazon EMR and AWS Glue serverless Spark environments, and monitor them using Spark UI. Use the built-in data preparation capability to visualize data, identify data quality issues, and apply recommended solutions to improve data quality. Automate your data preparation workflows quickly by scheduling your notebook as a job in a few steps. Store, share, and manage model features in a central feature store.

Learn more about preparing data at scale

Screenshot of a Jupyter notebook in Amazon SageMaker Studio showing commands and output for connecting to an Amazon EMR cluster using PySpark. The example demonstrates loading the SageMaker Studio Analytics extension, connecting to an EMR cluster, and initializing a SparkSession. The version of PySpark used is 3.2.0-amzn-0.

Quickly train models with optimized performance

Amazon SageMaker AI offers high-performing distributed training libraries and built-in tools to optimize model performance. You can automatically tune your models and visualize and correct performance issues before deploying the models to production.

Learn more about quickly training your models

Screenshot of an Amazon SageMaker experiment interface displaying a line chart of test loss values over training steps. The chart visualizes model performance, with 'Test:loss_last' on the y-axis and training 'step' on the x-axis, helping users track loss during the model training process.

Deploy models for optimal inference performance and cost

Deploy your models with a broad selection of AI infrastructure and deployment options to help meet your inference needs. SageMaker AI is fully managed and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, more effectively manage models in production, and reduce operational burden.

Learn more about scaling your AI model deployment

Screenshot of Amazon SageMaker Studio Inference Recommender interface showing instance recommendations for deployment, estimated costs, model latency, throughput optimization, and endpoint creation options for machine learning inference jobs.

Deliver high-performance production AI models

SageMaker AI provides purpose-built MLOps and governance tools to help you automate, standardize, and streamline documentation processes across the ML lifecycle. Using SageMaker AI MLOps tools, you can easily train, test, troubleshoot, deploy, and govern AI models at scale while maintaining model performance in production.

Learn more about high-performance MLOps

Screenshot of an Amazon SageMaker Studio interface showing a machine learning pipeline execution graph, with nodes representing steps such as data processing, training, model evaluation, and model creation. The interface displays a failed execution and key metrics like run time and status for the AbalonePipeline.

Get generative AI–powered assistance

Accelerate your AI model development velocity with AI assistance powered by Amazon Q Developer on JupyterLab and Code Editor. Use Amazon Q Developer inline code suggestions and chat-based assistance to receive how-to guidance, coding support, and troubleshooting steps on demand. Quickly get started and boost your productivity with this powerful tool at your fingertips.

Screenshot of Amazon SageMaker Studio showing an example of training an XGBoost machine learning model, with code cells for data loading, preprocessing, and model training, as well as AI assistant suggestions.

Accelerate ML and generative AI development

Explore apps from AWS Partners in Amazon SageMaker AI. Find, deploy, and use these apps and enjoy a seamless, fully managed experience with no infrastructure to provision or operate—all within the security and privacy of your SageMaker AI environment.

Learn more about Amazon SageMaker Partner AI apps