Data Science users initiate the process of model development by creating or importing Jupyter notebooks. The notebook will contain code to perform operations such as data access, data preparation, feature selection, model training and validation. As part of starting a new project, Data Science users need to be able to create & configure the notebook server environment that will serve as a foundation for all notebook files in the environment.

Draft reqs:

• Appropriate environment variables should be auto-populated in any new notebook server.
• Favorite settings auto-populated for one-click creation. Need to make it easy for users to reuse configuration definitions for NB server. Should include option to define all aspects of NB server config. When users create a new NB server, they could just select profile or template and it would automatically prefill all config elements.
• Easy access to settings while running, and when no longer running

Requirements for notebook server environment creation:

1. P2: The system must support the ability to connect the notebook server to AWS RDS (Relational Database Service) so notebooks can access relational data.  
2. P2: The system must support the ability to connect the notebook server to AWS Redshift so notebooks can access columnar data.
3. P2: The system must automatically populate connected services endpoints (eg. managed Kafka)to environment variables so they can be used in notebooks. Note: This assumes the user has selected an option to connect the notebook server to other services such as managed Kafka.
4. P2: The system must support the ability for a user to create multiple concurrent notebook servers. The idea is that users may need to work on multiple projects at the same time, and if they were all done using the same server, they could experience resource issues (eg. insufficient memory).