Why projects matter
An AI or ML system is built from code, data, and models that each evolve on their own schedule. Code changes through commits and pull requests; data refreshes on a pipeline; models get retrained or swapped. A project gives all of these a shared home with versioning and continuous delivery, so you can iterate rapidly across branches and still bring everything together into a working system you can keep improving.What a project contains
A project is defined in a Git repository and centers on a configuration file,obproject.toml, that names the project and the Anaconda Platform organization it belongs to:
main can deploy to a production perimeter while feature branches deploy to a development one.
Each project tracks the following assets:
Assets are references, not storage. An asset points at the code, data, or model itself, wherever it lives: the Git repository, an artifact a flow produced in the data plane, or an external system the project connects to.
Data and model assets add a layer of metadata and tracking on top of the artifacts a flow produces. They answer what a project’s key inputs and outputs are, which flows produce and consume them, and when each was last refreshed. Assets are scoped to a project branch, so you can evaluate different models or datasets in isolation and compare them across branches.