This issue contains the usage design, and will serve as the starting point of the database design.
The idea behind this project is to bring a simple but effective machine learning tracking system. The initial idea is the next:
- A User is the entity reflecting an individual capabilities
- Users can have access to projects
- Users can't have individual access to anything below a project
- There are two types of users: Administrator and Regular
- Administrators are able to manage projects and are non-limited by permissions
- Regulars actions to anything below a project are limited by permissions. They can't manage projects
- The permissions are: create, read, update, and delete
- A Project is an user directory of experiment executions
- Projects contain the user permissions
- Projects can contain experiments
- A project deletion brings a cascade deletion
- An Experiment is the core entity in the machine learning project structure
- Experiments can contain resources (documents, artifacts, graphics, etc...)
- Experiments can contain iterations
- An experiment deletion brings a cascade deletion
- An Iteration is the product of a model execution. It saves results information
- Iterations can contain parameters and metrics
- A parameter is a value passed at the start of the model training process
- A metric is a measurement got during or after the model training process
- Iterations can contain results
- Iterations can contain notes
- An iteration deletion brings a cascade deletion
This issue contains the usage design, and will serve as the starting point of the database design.
The idea behind this project is to bring a simple but effective machine learning tracking system. The initial idea is the next: