Are machine learning solutions useful
Posted on: May 21, 2018
You can now access all of the Jupyter sample notebooks provided by Amazon SageMaker from a new SageMaker Examples tab on the Jupyter user interface console to help you get started with machine learning faster. These examples cover topics such as machine learning fundamentals, in-depth instructions on specific algorithms and frameworks, advanced SageMaker features, and integration with Apache Spark. Up until now, you had to navigate to each directory in the Jupyter UI to look at the examples, duplicate the notebook, and move it to your home directory to customize it. Now, with the addition of the nbexamples plugin, Amazon SageMaker extends the Jupyter user interface to make the sample notebooks a streamlined process. From the list of notebooks grouped by category, you can view a read-only copy of the notebook to examine it further before using it. Once you've selected the notebook that best suits your machine learning solution, a single click in the Jupyter user interface copies it to the home directory of 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 Jupyter UI plug-in is available today in Amazon SageMaker in the US East (N.Virginia), US East (Ohio), EU (Ireland), and US West (Oregon) AWS Regions. For more information on using the sample notebooks with the nbexamples plug-in, see the Amazon SageMaker documentation.
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