ValidMind Library and ValidMind Platform

Published

December 23, 2025

ValidMind AI Risk Platform provides two main product components, the ValidMind Library and the ValidMind Platform.

The ValidMind Library is a Python library of tools and methods designed to automate generating model documentation and running validation tests. The library is designed to be platform agnostic and integrates with your existing development environment.

For Python developers, a single installation command provides access to all the functions:

%pip install validmind

The ValidMind Platform is an easy-to-use web-based interface that enables you to track the model lifecycle:

  • Customize workflows to adhere to and oversee your governance processes.
  • Review and edit the documentation and test metrics generated by the library.
  • Collaborate with and capture feedback from model developers and model validators.
  • Generate validation reports and approvals.
model documentation
A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses.

Within the realm of model risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.

validation report
A formal document produced after a model validation process, outlining the artifacts, assessments, and recommendations related to a specific model’s performance, appropriateness, and limitations. Provides a comprehensive review of the model’s conceptual framework, data sources and integrity, calibration methods, and performance outcomes.

Within model risk management, the validation report is crucial for ensuring transparency, demonstrating regulatory compliance, and offering actionable insights for model refinement or adjustments.

documentation template
Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.
validation report template

Serves as a standardized framework for conducting and documenting model validation activities. It outlines the required sections, recommended analyses, and expected validation tests, ensuring consistency and completeness across validation reports. The template helps guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.

​ValidMind templates come with pre-defined sections, similar to test placeholders, including boilerplates and spaces designated for test results, evidence, or artifacts.

test
A function contained in the library, designed to run a specific quantitative test on the dataset or model. Test results are sent to the ValidMind Platform to generate the model documentation according to the template that is associated with the documentation.

Tests are the building blocks of ​ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.

metrics, custom metrics
Metrics are a subset of tests that do not have thresholds. Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.

In the context of ​ValidMind’s Jupyter Notebooks, metrics and tests can be thought of as interchangeable concepts.

inputs
Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:
  • model: A single model that has been initialized in ​ValidMind with vm.init_model(). See the Model Documentation or the for more information.
  • dataset: Single dataset that has been initialized in ​ValidMind with vm.init_dataset(). See the Dataset Documentation for more information.
  • models: A list of ​ValidMind models - usually this is used when you want to compare multiple models in your custom tests.
  • datasets: A list of ​ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: [Run tests with multiple datasets(/notebooks/how_to/run_tests_that_require_multiple_datasets.ipynb)])
parameters
Additional arguments that can be passed when running a ​ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.
outputs
Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.
test suite
A collection of tests which are run together to generate model documentation end-to-end for specific use cases.

For example, the classifier_full_suite test suite runs tests from the tabular_dataset and classifier test suites to fully document the data and model sections for binary classification model use cases.

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