Amazon SageMaker Studio

The fully integrated development environment (IDE) for machine learning
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Why SageMaker Studio?

Amazon SageMaker Studio is an integrated development environment(IDE) that provides a single web-based visual interface where you canaccess purpose-built tools to perform all machine learning (ML)development steps, from preparing data to building, training, and deploying your ML models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, and deploy models to production without leaving SageMaker Studio. It allows you to quickly switch environments and collaborate seamlessly within your organization to build ML models at scale.

Introduction to Amazon SageMaker Studio

Why SageMaker Studio?

Amazon SageMaker Studio is an integrated development environment(IDE) that provides a single web-based visual interface where you canaccess purpose-built tools to perform all machine learning (ML)development steps, from preparing data to building, training, and deploying your ML models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, and deploy models to production without leaving SageMaker Studio. It allows you to quickly switch environments and collaborate seamlessly within your organization to build ML models at scale.

Introduction to Amazon SageMaker Studio

How it works

How Amazon SageMaker Studio works

How it works

How Amazon SageMaker Studio works

Benefits of SageMaker Studio

Amazon SageMaker Studio offers a unified experience for ML development. ML teams can perform the complete ML workflow in a single web-based visual interface.
Boost ML development productivity with developer tools in SageMaker Studio. Generate, debug, and explain the source code with Amazon CodeWhisperer, the generative AI-powered code generator and companion. Conduct security and code quality scans with Amazon CodeGuru, the security tool that provides intelligent recommendations to fix vulnerabilities.
Build ML models, including foundation models (FMs), in minutes with access to hundreds of popular publicly available models and over 15 prebuilt solutions. Create ML models with your own data with just a few clicks.
Access to the most comprehensive set of tools for each step of ML development, from preparing data to building, training, and deploying ML models. Quickly move between steps to fine-tune your models, replay training experiments, and scale to distributed training directly from SageMaker Studio notebooks.

Use Cases

Create shared spaces in SageMaker Studio where your teams can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review results together to immediately understand how a model performs without passing information back and forth.
Unify your end-to-end ML development in SageMaker Studio with the most comprehensive ML tools all in one place. SageMaker offers high-performing MLOps tools to help you automate and standardize ML workflows and governance tools to support transparency and auditability across your organization.
Build foundation models faster in SageMaker Studio with access to a wide range of publicly available models, notebooks backed by high performance compute for fine-tuning, and ability to scale to distributed training directly from Studio notebooks.
SageMaker Studio offers a unified experience to perform all data analytics and ML workflows. Create, browse, and connect to Amazon EMR clusters. Build, test, and run interactive data preparation and analytics applications with Amazon Glue interactive sessions. Monitor and debug Spark jobs using familiar tools such as Spark UI – all right from SageMaker Studio notebooks.