April 24, 2025
Unified version 25.03.05
We’ve released the ability to manage model stakeholder types within the ValidMind Platform, support for custom configuration of test-driven blocks via the ValidMind Library, an updated Python API reference within our documentation, and more.
Release highlights — 25.03.05
ValidMind Library (v2.8.13)
New introduction for model development notebook series
We’ve revamped our old Introduction for model developers notebook into a series of four introductory notebooks — ValidMind for model development:
- Set up the ValidMind Library
- Start the model development process
- Integrate custom tests
- Finalize testing and documentation
These new notebooks break down using ValidMind for your end-to-end model documentation process based on common model development scenarios:
- Learn the basics of the ValidMind Library with these interactive notebooks designed to introduce you to basic ValidMind concepts and get you familiar with tasks such as how to work with documentation templates, running and logging tests with ValidMind, and more.
- After you’ve completed your learning journey with these notebooks, you’ll have a fully documented sample model ready for review.
ValidMind Platform (v1.31.10)
Ability to manage model stakeholder types
We’ve introduced the ability to define custom model stakeholder types, allowing you to control granular permissions on each model in your inventory:
- Model stakeholders determine specific responsibilities and access levels for model review and approval processes for each model in your model inventory, such as read or edit access to model inventory fields.
- Each model stakeholder also belongs to user groups1 which determine which models they can see, and have user roles2 with attached role permissions3 which define the level of access they have to overarching ValidMind Platform features.
Documentation
Improved Python API reference
We’ve completely redesigned our ValidMind Library Python API reference using the same tools we use to produce the rest of our product documentation, allowing us to more easily keep this information up to date and ensure its accuracy.
- Now featuring more intuitive navigation wrapped in a familiar sidebar, enhanced code signature styling, and integration with our main docs site search, these improvements aim to empower users to maximize the potential of the ValidMind Library.
- The updated reference structure mirrors the Python package layout, ensuring backward compatibility with our old Python API reference while providing a more reader-friendly experience.
Enhancements
ValidMind Library (v2.8.13)
Support for display configuration of test-driven blocks5
You can now customize the display of test results when inserted as test-driven blocks in the ValidMind Platform when logging results via the ValidMind Library with result.log()
.
You can now toggle the display of the following attributes:
hideTitle
: TitlehideText
: DescriptionhideParams
: ParametershideTables
: TableshideFigures
: Figures
For example, to show figures but hide tables:
= vm.tests.run_test(
test "validmind.data_validation.TabularDescriptionTables:raw_dataset",
={
input_grid"dataset": [vm_raw_dataset],
},
)={"hideFigures": False, "hideTables": True}) test.log(config
Bug fixes
ValidMind Platform (v1.31.10)
How to upgrade
ValidMind Platform
To access the latest version of the ValidMind Platform,8 hard refresh your browser tab:
- Windows:
Ctrl
+Shift
+R
ORCtrl
+F5
- MacOS:
⌘ Cmd
+Shift
+R
OR hold down⌘ Cmd
and click theReload
button
ValidMind Library
To upgrade the ValidMind Library:9
In your Jupyter Notebook:
Then within a code cell or your terminal, run:
%pip install --upgrade validmind
You may need to restart your kernel after running the upgrade package for changes to be applied.