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 administrator has 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.
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 administrator, launch a Notebook product using the same project name that was used to create the environment.
Assume Roleon the next screen.
NotebookOwnerEmailand a username for
Nextand on the next 2 screens click
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.
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.
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.
Execute the steps in each of the cells of the notebook carefully, reading the text as you work through these notebooks until you get to ## Lab 4: Train Without VPC Configured.
Once that is complete, finish the rest of this notebook.
Having completed the steps in both notebooks, you have learned how to build, train, deploy, and monitor models using Amazon SageMaker with security best practices.