Easily Access Amazon SageMaker Example Notebooks Through the Jupyter Interface
You can now access all of the example Jupyter notebooks provided through Amazon SageMaker from a new “SageMaker Examples” tab on the Jupyter interface console to help you get started using machine learning even faster. These examples cover topics like machine learning fundamentals, in-depth instruction on specific algorithms and frameworks, advanced SageMaker features, and integration with Apache Spark. Until now, you had to navigate to each directory in the Jupyter interface to look at the examples, duplicate the notebook, and move it to your home directory to customize. Now, with the addition of the nbexamples plug-in, Amazon SageMaker extends the Jupyter interface to make discovering the sample notebooks a more streamlined process. From the list of notebooks grouped by category, you can preview a read-only copy of the notebook to examine it in more detail before use. Once you’ve selected the notebook most applicable to your machine learning solution, a single click in the Jupyter interface copies it to the home directory on your Jupyter notebook instance with the name you choose. You can then modify the notebook for your particular use case and run it to build, train, and deploy your machine learning model.
You can now access all of the example Jupyter notebooks provided through Amazon SageMaker from a new “SageMaker Examples” tab on the Jupyter interface console to help you get started using machine learning even faster. These examples cover topics like machine learning fundamentals, in-depth instruction on specific algorithms and frameworks, advanced SageMaker features, and integration with Apache Spark. Until now, you had to navigate to each directory in the Jupyter interface to look at the examples, duplicate the notebook, and move it to your home directory to customize. Now, with the addition of the nbexamples plug-in, Amazon SageMaker extends the Jupyter interface to make discovering the sample notebooks a more streamlined process. From the list of notebooks grouped by category, you can preview a read-only copy of the notebook to examine it in more detail before use. Once you’ve selected the notebook most applicable to your machine learning solution, a single click in the Jupyter interface copies it to the home directory on your Jupyter notebook instance with the name you choose. You can then modify the notebook for your particular use case and run it to build, train, and deploy your machine learning model.
The new plug-in to the Juptyer interface is available in Amazon SageMaker today in the US East (N. Virginia), U.S. East (Ohio), EU (Ireland) and U.S West (Oregon) AWS regions. For more information on how to use the example notebooks with the nbexamples plugin, visit the Amazon SageMaker documentation.