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sientia-dataops-model-manager
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6e8f87b2a3fb1966bab4855223d9780f103d491d
sientia-dataops-model-manager
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tests
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utils
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models
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Bruno Domingues
6e8f87b2a3
SIENTIAPDE-1430: Implement advanced model training capabilities and enhanced data preprocessing. This includes support for Polynomial Regression with configurable degree and interaction terms, flexible per-variable lag configurations, and new data filtering options by date range and removed intervals. Comprehensive business validations are now enforced for all parameters, and MLflow logging has been extended to capture these detailed configurations. Additionally, Reduced Coulomb Energy (RCE) metrics are added for drift detection, with a new changelog documenting all pipeline parameter updates.
2025-12-17 21:39:43 -03:00
..
__init__.py
SIENTIAPDE-1250: Refactor: Remove 'laborious' directory from test structure and update README.md accordingly.
2025-10-07 10:02:38 -03:00
test_experiment_status.py
SIENTIAPDE-1309: Update README with Helm instructions and refactor experiment status messages. Also, update values.yaml with new image and configurations.
2025-11-06 15:34:03 -03:00
test_init.py
SIENTIAPDE-1309: Update README with Helm instructions and refactor experiment status messages. Also, update values.yaml with new image and configurations.
2025-11-06 15:34:03 -03:00
test_train_model_params.py
SIENTIAPDE-1430: Implement advanced model training capabilities and enhanced data preprocessing. This includes support for Polynomial Regression with configurable degree and interaction terms, flexible per-variable lag configurations, and new data filtering options by date range and removed intervals. Comprehensive business validations are now enforced for all parameters, and MLflow logging has been extended to capture these detailed configurations. Additionally, Reduced Coulomb Energy (RCE) metrics are added for drift detection, with a new changelog documenting all pipeline parameter updates.
2025-12-17 21:39:43 -03:00
test_train_model_result.py
SIENTIAPDE-1430: Implement advanced model training capabilities and enhanced data preprocessing. This includes support for Polynomial Regression with configurable degree and interaction terms, flexible per-variable lag configurations, and new data filtering options by date range and removed intervals. Comprehensive business validations are now enforced for all parameters, and MLflow logging has been extended to capture these detailed configurations. Additionally, Reduced Coulomb Energy (RCE) metrics are added for drift detection, with a new changelog documenting all pipeline parameter updates.
2025-12-17 21:39:43 -03:00