- record
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A tool tracked in the ValidMind Platform inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.
- model
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SR 26-2 (which supersedes SR 11-7) defines a model as a “complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates.” Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model.
Within ValidMind, a model is a type of record tracked in the inventory.
- documentation, model documentation
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A structured and detailed record pertaining to a record (such as a model), encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses.
- validation report
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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.
- ongoing monitoring, ongoing monitoring report, ongoing monitoring plan, monitoring plan
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A comprehensive and structured periodic report assessing the record’s performance and compliance over time, ensuring it remains valid under changing conditions. Monitoring includes key elements such as data sources, inputs, performance metrics, and periodic evaluations, ensuring transparency and visibility of the record’s performance in the production environment.
- document template
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Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite to help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.
- documentation template
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A default ValidMind document type that serves as a standardized framework for developing and documenting records (such as models), including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, documentation templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.
- validation report template
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A default ValidMind document type that serves as a standardized framework for conducting and documenting validation, including sections designated for attaching test results, evidence, or artifacts (findings). By outlining required documentation, recommended analyses, and expected validation tests, validation report templates ensure consistency and completeness across validation reports and help guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.
- monitoring template, monitoring report template
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A default ValidMind document type that serves as a standardized framework for ongoing monitoring, including sections designated for test results, performance metrics, and drift analyses. By outlining required monitoring checks and expected routine tests, monitoring templates ensure consistency and completeness across monitoring reports and help guide owners through a systematic monitoring process while promoting early detection of performance degradation.
- test
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A function contained in the library, designed to run a specific quantitative test on the dataset or record (such as a model). Test results are logged to the ValidMind Platform, where they are attached to documents.
Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.
- test suite
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A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)
- metrics, custom metrics
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Metrics are a subset of tests that do not have thresholds. Custom metrics are functions that you define to evaluate your record (such as a 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
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Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:
- model: A single record (such as a model) that has been initialized in ValidMind. Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind. Refer to the
vm.init_model() function for more information.
- dataset: A single dataset that has been initialized in ValidMind. Refer to the
vm.init_dataset() function for more information.
- models: A list of ValidMind records — usually this is used when you want to compare multiple records 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)
- parameters
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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
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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.