JupyterLab
Code Editor, based on Code-OSS
RStudio
Access and evaluate FMs
Prepare data at scale
Simplify your data workflows with a unified environment for data engineering, analytics, and ML. 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 ML model features in a central feature store.
Train models quickly with optimized performance
Amazon SageMaker 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.
Deploy models for optimal inference performance and cost
Deploy your models with a broad selection of ML infrastructure and deployment options to help meet your ML inference needs. It is fully managed and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden.
Deliver high-performance production ML models
SageMaker provides purpose-built MLOps and governance tools to help you automate, standardize, and streamline documentation processes across the ML lifecycle. Using SageMaker MLOps tools, you can easily train, test, troubleshoot, deploy, and govern ML models at scale while maintaining model performance in production.