Summary

Let’s look back on what you’ve accomplished during the labs in this section.


In this series of labs you used many AWS services to support your machine learning process. As the Cloud Platform Engineering team you created a secure network environment with corrective controls for the data science administration team. You also created a self-service product that codified best practices and allowed the Data Science Administration team to support the data science project teams without needing expansive permissions in AWS.

As the Data Science Administration team you used the enviornment and product created by the CPE to support the Data Science project team. You created an IAM role for the data science team, an encryption key, and Amazon S3 buckets for the project team to store their data and models. Finally you created a self-service product that the data science team could use to provision Jupyter notebooks for themselves, also without needing extensive permissions into AWS.

Then, as a data scientist and a member of the project team, you created a Jupyter notebook instance and trained a machine learning model in accordance with your security best practice. This demonstrated the corrective control deployed and managed by the Cloud Platform Engineering team as well as the preventive control later deployed by the Data Science Administration team.

This module relied on the following key services that you should now be familiar with:

To find out more about any of these services please visit their documentation or visit AWS Security Workshops for more great security-focused workshops.