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Model Validation Framework

$39

Model testing, data drift detection, performance monitoring, and validation gates for CI/CD.

📁 8 files🏷 v1.0.0
MarkdownYAMLJSONCI/CD

📁 File Structure 8 files

model-validation-framework/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-drift-detection-pipeline.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Model Validation Framework

Comprehensive model testing and validation toolkit with data drift detection, performance monitoring, and automated validation gates. Ensure your models meet quality standards before and after deployment.

What's Included

  • Model testing framework with unit and integration test patterns
  • Data drift detection using statistical tests
  • Performance monitoring dashboards and alert configs
  • Validation gate templates for CI/CD pipelines
  • Bias and fairness metric computation
  • Schema validation for model inputs and outputs
  • Regression testing against baseline models

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the validation suite on your model
python -m validation.run --model-path ./model.pkl --data ./test_data.csv

Prerequisites

  • Python 3.9+
  • scikit-learn, scipy, numpy
  • Great Expectations (optional, for data validation)
  • Evidently AI (optional, for drift detection)

Contents


model-validation-framework/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # Model Validation Framework - Example Configuration # Copy this file to config.yaml and update values for your environment validation: model_path: "./model.pkl" test_data_path: "./data/test.csv" target_column: "label" performance: metrics: - "accuracy" - "f1_score" - "roc_auc" thresholds: accuracy: 0.85 f1_score: 0.80 drift: enabled: true reference_data: "./data/reference.csv" method: "ks_test" # ks_test, chi2, psi p_value_threshold: 0.05 schema: enabled: true expected_columns: 10 check_nulls: true max_null_rate: 0.05 bias: enabled: false protected_attributes: ["gender", "age_group"] fairness_metric: "demographic_parity" logging: level: "INFO"
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