Your project team has been presented with IAM roles and a Service Catalog Portfolio to allow your team to self service and obtain resources to support your efforts. Following the steps below use this portfolio to create a Jupyter notebook instance for yourself.
Click into the details for the data science environment product you provisioned in the last step and find the link to assume the role of a data science user. After assuming this role return to the Service Catalog console and launch an Amazon SageMaker Jupyter notebook.
You will land at a Provisioned Product page while the Service Catalog creates your Jupyter Notebook. Periodically click Refresh until the Status reads
Succeeded. This should take about 5 minutes to launch your notebook.
Every SageMaker notebook has permissions granted to it to be able to access, create, and delete AWS resources and APIs. These permissions are granted through an IAM role associated with the Jupyter notebook’s EC2 instance. An example of the permissions associated with the notebook are highlighted in the next set of labs.
The IAM role assigned to your notebook has been created just for your notebook and represents an association of the Jupyter notebook to both yourself and your project. This will be represented in audit logs, identifying that actions were taken by your Jupyter notebook and allow for easy tracking of which notebook, for which project, belonging to which project team member performed an action on AWS resources.
After the notebook product has finished provisioning you can open it by clicking the
NotebookUrl link provided as an Output in the provisioned product detail. With your Jupyter notebook open, familiarize yourself with the web interface and open the Notebook kernel named
01_SageMaker-DataScientist-Workflow.ipynb. Don’t forget to reference back to the Jupyter cheat sheet for a quick reference if you need one.
NotebookUrlto launch the Jupyter notebook interface.
When its open the Notebook kernel should use the
conda_python3 kernel. If Jupyter asks you to set the kernel select
conda_python3 and if Jupyter displays an error, reload the Jupyter page by clicking your browser Refresh button.
You have now created a Jupyter notebook dedicated to yourself as a member of the project team. With the notebook open you are ready to take on the next set of labs where you will use the notebook and the data science environment to engineer a feature set, train a model, deploy the model, and then monitor the model for performance or drift.