Create a Project

How to create a Project for your monitoring use case.

What is a Project?

A Project helps gather all Reports and Test Suites associated with the same use case. Each Project has a dedicated monitoring dashboard and snapshot storage.

Should you have one Project for one ML model? You will often create one project per ML model or dataset, but this is not a strict rule. For example, you can log the performance of a champion and challenger models to the same Project. Or, store data on related models (such as demand forecasting models by country) in one Project and use tags to organize them. You can also set up your monitoring for any data pipeline or dataset.

Once you create a Project, you can connect to it from your Python environment to send the data or edit the dashboards. In Evidently Cloud, you can work both via API and a graphic user interface.

Create a Project

Add a new Project

To create a Project inside a workspace ws, assign a name and description, and save the changes:

project = ws.create_project("My project name")
project.description = "My project description"
project.save()

Project ID. Once you run create_project, you will see the Project ID. You can later use it to reference the Project. You can also copy the Project ID directly from the UI: it appears above the monitoring dashboard.

Add a Team Project

Team management is a Pro feature available in the Evidently Cloud.

You can associate a Project with a particular Team, such as a "Marketing team" for related ML models. A Project inside the Team will be visible to all Team members.

You must create a Team before adding a Project. Navigate to the “Teams” section in the left menu, and add a new one. You can add other users to this Team at any point after creating it.

After creating the team, copy the team_ID from the team page. To add a Project to a Team, reference the team_id when creating the Project:

project = ws.create_project("Add your project name", team_id="TEAM ID")
project.description = "Add your project description"
project.save()

What's next? Head to the next section to see how to send data to a Project.

Connect to a Project

To connect to an existing Project from your Python environment (for example, if you first created the Project in the UI and now want to send data to it), use the get_project method.

project = ws.get_project("PROJECT_ID")

Save changes

After you make any changes to a Project via API (such as editing description or adding new monitoring panels), you must use the save() command:

project.save()

Browse Projects

You can see all available Projects on the monitoring homepage, or request a list programmatically. To get a list of all Projects in a workspace ws, use:

ws.list_projects()

To find a specific Project by its name, use the search_project method:

ws.search_project("project_name")

[DANGER] Delete Project

You are deleting the data in a Project. If you delete a Project, you will delete all the associated snapshots stored in a Project.

To delete the Project and all the data inside it:

# ws.delete_project("PROJECT ID")

Project parameters

Each Project has the following parameters.

ParameterDescription

name: str

Project name.

id: UUID4 = Field(default_factory=uuid.uuid4)

Unique identifier of the Project. Assigned automatically.

description: Optional[str] = None

Optional description. Visible when you browse Projects.

dashboard: DashboardConfig

Configuration of the Project dashboard. It describes the monitoring Panels that appear on the dashboard. Note: Explore the Dashboard Design section for details. There is no need to explicitly pass DashboardConfig as a parameter if you use the .dashboard.add_panel method to add Panels.

date_from: Optional[datetime.datetime] = None

Start DateTime of the monitoring dashboard. By default, Evidently shows data for all available periods based on the snapshot timestamps. You can set a specific date or a relative DateTime. For example, to refer to the last 30 days: from datetime import datetime, timedelta datetime.now() + timedelta(-30) When you view the dashboard, the data will be visible from this start date. You can switch to other dates in the interface.

date_to: Optional[datetime.datetime] = None

End datetime of the monitoring dashboard. Works the same as above.

What’s next?

Once you create or connect to a Project, you can:

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