Lab 3: Deploy a Jupyter Notebook

At this point, the cloud platform engineering team has built a self-service mechanism to provision secure environments to host data science projects. The project administrators have provisioned resources using the self-service mechanism for your team to work, and they have provided you with a self-service mechanism to enable you to provision SageMaker notebooks. Now, use these resources to provision a Jupyter notebook and start developing your ML solution.


Launch the notebook product

Navigate to the AWS Service Catalog console and on go to the detail page for the recently provisioned data science environment. Use the hyperlink labeled AssumeProjectUserRole under Outputs to assume the role of a data science user. Assume the role and visit the Service Catalog product listing. Using the notebook product defined for you by the project administrators, launch a Notebook product using the same project name that was used to create the environment.

Step-by-step instructions

Provisioned Notebook Product

Access the notebook

After the notebook has launched successfully you can open it by clicking the NotebookUrl hyperlink in the Outputs section of the provisoined notebook details page. With your Jupyter notebook open, familiarize yourself with the web interface and open the Notebook kernel named 00_SageMaker-SysOps-Workflow. Don’t forget to reference back to the Jupyter cheat sheet for a quick reference if you need one.

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 will now learn about methods for implementing detective and corrective controls on AWS. In the next lab you will return to the Jupyter notebook to test the detective controls.