Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Amazon SageMaker removes the complexity that holds back developer success with each of these steps; indeed, it includes modules that can be used together or independently to build, train, and deploy your machine learning models.
In this section, we will walk you through accessing the AWS Console, Amazon SageMaker Studio, and cloning this repository for executiong next modules.
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Sign into the AWS Management Console using the Event Engine dashboard at https://dashboard.eventengine.run and the hashcode provided by the workshop instructors. [Or access it at https://console.aws.amazon.com/ if you are using your own AWS account].
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In the upper-right corner of the AWS Management Console, confirm you are in the desired AWS region. For the instructions of these workshop we will assume using the EU West (Ireland) [eu-west-1], but feel free to change the region at your convenience.
The only constraints for changing AWS region are that we keep consistent the region settings for all services used and services are available in the selected region (please check in case you plan to execute this workshop in another AWS region).
Amazon SageMaker Studio has been pre-configured in the AWS Account. In this section we will open Studio and clone the repository.
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In the AWS Management Console, search for "SageMaker" and select Amazon SageMaker in the results.
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You’ll be placed in the Amazon SageMaker dashboard. Click on Amazon SageMaker Studio in the left menu and then on the Open Studio button associated to the defaultuser.
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Amazon SageMaker Studio will load (it can take a few minutes). Then you will be redirected to the Studio interface.
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In the File menu, choose New >> Terminal
This will open a terminal window in the Jupyter interface.
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Execute the following commands in the terminal
git clone https://github.com/giuseppeporcelli/end-to-end-ml-sm.git
The repository will be cloned to your use home and will appear in the file browser panel as shown below:
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Browse to the folder 02_data_exploration_and_feature_eng and open the file 02_data_exploration_and_feature_eng.ipynb to start the data exploration, preparation and feature engineering steps.
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If a kernel is not automatically selected for your notebook, choose the kernel by clicking on the Kernel button on the top-right and them selecting the Python 3 (Data Science) kernel as shown below:
