Collaborating on Projects with Cloudera AI

Project Collaborators

To collaborate closely with trusted colleagues on a particular project, you can add them to the project as collaborators. This approach is particularly recommended for projects created under your personal account. Any member of your organization can be added as a project collaborator.

Project Visibility Levels: When creating a project in your personal context, Cloudera AI requires you to assign one of the following visibility levels to the project : Private or Public.

Public projects allow read-level access to everyone with access to the Cloudera AI application.
Private projects require you to explicitly add collaborators to grant access.

Project Collaborator Access Levels: You can grant project collaborators the following levels of access: Viewer, Operator, Contributor, Administrator.

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Collaborating Securely on Projects

Before adding project collaborators, it is important to note that assigning the Contributor or Administrator role grants write access to your data in Cloudera. This is because both Contributors and project Administrators have write access to all your project code, including library code that you might not be actively reviewing. For example, a Contributor or an Administrator could modify project files to include code that deletes data on the cluster. If you later run the modified code in a session, it might appear as if you were the one who deleted the data.

By default, project collaborators also have access to all actively running sessions and jobs. This creates a potential risk, as a malicious user could impersonate you by accessing one of your active sessions. To mitigate this risk, it is important to limit project access to trusted collaborators only. Site Administrators can further enhance security by configuring settings to ensure that only session creators are allowed to execute commands within their own active sessions.

Taking these factors into account, Cloudera recommends using Git for secure collaboration on shared projects.

Teams

Users collaborating on more than one project can create a Team to simplify project management and enhance collaboration. Teams enable streamlined administration, with projects owned by the team rather than an individual user. Only team members can be added as collaborators to projects created within the team context. Team Administrators have the ability to add or remove members at any time and assign different access permissions to each member.

Team Member Access Levels: You can grant team members the following access levels: Viewer, Operator, Contributor, Administrator.

ML Business User

The ML Business User role is designed for users who only need to view any applications that are created within Cloudera AI. This role is ideal for employees outside the Data Science team who do not require advanced access to workbenches and projects but need to access the output of Data Science workflows. MLBusinessUser seats are available for purchase separately.

Forking Projects

You can fork another user's project by clicking Fork on the Project page. Forking creates a new project under your account that contains all the files, libraries, configurations, jobs, and dependencies between jobs from the original project.

Creating sample projects that other users can fork helps to bootstrap new projects and encourage common conventions.

Collaborating with Git

Cloudera AI provides seamless access to Git projects, enabling users to take full advantage of version control and collaboration features. Whether working independently, or as part of a team, you can leverage all the benefits of version control and collaboration with Git from within Cloudera AI. Teams already using Git for collaboration can continue their existing practices, with each team member creating a separate Cloudera AI project linked to the central Git repository.

For all but the simplest projects, Cloudera recommends using Git for version control. Cloudera AI is designed to support workflows similar to local development environments, and for most data scientists and developers, this includes leveraging Git for efficient collaboration and code management.