Amazon SageMaker Studio

The first fully integrated development environment (IDE) for machine learning

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10x. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive. All ML development activities including notebooks, experiment management, automatic model creation, debugging, and model and data drift detection can be performed within SageMaker Studio.

Key features

Elastic and Shareable Notebooks

Managing compute instances to view, run, or share a notebook is tedious. Amazon SageMaker Studio Notebooks are one-click Jupyter notebooks that can be spun up quickly. The underlying compute resources are fully elastic, so you can easily dial up or down the available resources and the changes take place automatically in the background without interrupting your work. You can also share notebooks with others in a few clicks. They will get the exact same notebook, saved in the same place.

New!

Scalable data preparation using notebooks

You can visually browse, discover, and connect to Apache Spark data processing environments running on Amazon EMR from your SageMaker Studio notebooks with a few clicks. Once connected, you can interactively query, explore, and visualize data, and run Spark jobs using the language of your choice (SQL, Python, and Scala) to build end-to-end data preparation and ML workflows.

Scalable Experimentation

While experimenting with different combination of inputs to fine tune models, you can launch an experiment leaderboard alongside your notebooks. The leaderboard automatically tracks, sorts, and ranks all the experiments. With a glance, you can easily compare and identify the best performing model.

Quick to Start

Amazon SageMaker Studio includes a machine learning launcher with over 150 popular open source models and over 15 pre-built solutions for common use cases such as churn prediction and fraud detection so you can build your first model in just a few minutes. You can also use Amazon SageMaker AutoPilot to create ML models with your own data in a few clicks.

Bring Your Own Containers

Amazon SageMaker Studio Notebooks provide a set of built-in images for popular data science and deep learning frameworks such as Tensorflow, MXNet, PyTorch, and compute options to run notebooks. You can also register custom built images and kernels, and make them available to all users sharing a SageMaker Studio domain. With a custom image, you can spin up notebooks using specific versions of popular deep learning frameworks.

Deep Learning

Amazon SageMaker Studio supports many popular frameworks for deep learning such as TensorFlow, Apache MXNet, PyTorch, and more. These frameworks are automatically configured and optimized for high performance.

Amazon SageMaker Studio