Amazon SageMaker Studio – A Fully Integrated Development Environment For Machine Learning

Learn all about Amazon SageMaker Studio, a single, web-based visual interface for the complete machine learning workflow. With SageMaker Studio, you can quickly upload data, create new notebooks, train & tune models, and deploy these models, all in a single interface.

Learn more about Amazon SageMaker at https://go.aws/3g3Jc7l​
Reference Link: https://aws.amazon.com/blogs/aws/amazon-sagemaker-studio-the-first-fully-integrated-development-environment-for-machine-learning/

Amazon SageMaker Studio lets you manage your entire ML workflow through a single pane of glass. Let me give you the whirlwind tour!

With Amazon SageMaker Notebooks (currently in preview), you can enjoy an enhanced notebook experience that lets you easily create and share Jupyter notebooks. Without having to manage any infrastructure, you can also quickly switch from one hardware configuration to another.

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.

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.

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.

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