Amazon SageMaker Model Building

Build machine learning models efficiently

Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes.

Key features

One-click Jupyter Notebooks

Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working with in seconds. 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. Amazon SageMaker also enables one-click sharing of notebooks. All code dependencies are automatically captured, so you can easily collaborate with others. They’ll get the exact same notebook, saved in the same place.

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Bring your existing RStudio licenses and lift-and-shift the RStudio environments to Amazon SageMaker easily and securely. RStudio on Amazon SageMaker provides you with a familiar RStudio IDE with on-demand cloud compute resources. You can launch RStudio with a single click from Amazon SageMaker because it is fully managed, and R developers can dial up compute from within the same interface reducing interruptions to work and improving productivity. Multi-lingual developers can switch between the RStudio and SageMaker Studio notebooks for both R and Python workloads. All the work, including code, datasets, repositories, and other artifacts, is automatically synchronized between the two environments through the default Amazon Elastic File System (Amazon EFS) storage.

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Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning models, based on your data while allowing you to maintain full control and visibility. You then can directly deploy the model to production with just one click or iterate to improve the model quality.

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Pre-built solutions for open-source models

Amazon SageMaker JumpStart helps you quickly get started with ML using pre-built solutions that can be deployed with just a few clicks. SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models.

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Optimized for major frameworks

Amazon SageMaker is optimized for many popular deep learning frameworks such as TensorFlow, Apache MXNet, PyTorch, and more. Frameworks are always up-to-date with the latest version, and are optimized for performance on AWS. You don’t need to manually setup these frameworks and can use them within the built-in containers.

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Local mode

Amazon SageMaker enables you to test and prototype locally. The Apache MXNet and TensorFlow Docker containers used in SageMaker are available on GitHub. You can download these containers and use the Python SDK to test scripts before deploying to training or hosting.

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