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This page outlines the core concepts of the Bitfount platform, projects, tasks, models and datasets.


Bitfount supports the convening of collaborators within the context of a Project. A project can be thought of as a collection of settings that dictate the who, what, and how of the data collaboration taking place within it. Projects are managed by a project owner who can invite participants to join, link their dataset and run machine learning and analysis tasks. Learn more about projects here.


A Bitfount task is a specification of the algorithm to run, and can include the use (inference or training) of AI models as well as other data science operations. Tasks are defined and templated by by data scientists via the SDK, making them available for repeatable use in one, or several projects. Once assigned to a project, task code can be inspected by any participating user. Learn more about tasks here.


Models are a key component of tasks and refer to programs defined by data scientists that analyse datasets to find patterns and make predictions. Models can be public and open to any user, or private. Private models require a licensing agreement before they can be utilised within a task assigned to a project. Learn more about models here.


Datasets are the collection of raw data connected by data custodians to the Bitfount platform. Datasets remain on the data custodian’s systems at all times and are not transferred or stored by Bitfount or any other collaborator’s systems unless explicitly agreed. Once connected, a data custodian is able to link their dataset to projects and run the associated data science task against it. Learn more about datasets here.

Next steps

Now you understand the core concepts of the Bitfount platform, you can now visit the guides relevant to your capacity within a project. Please see either For Project Owners, For Data Custodians or For Data Scientists.

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