Bruno Domingues
06fd08dc70
SIENTIAPDE-1430: Introduce comprehensive integration testing with JSON-based scenarios and detailed README documentation. Enhance training workflow to support advanced model configurations, including polynomial regression with mandatory scaler validation. Ensure robust prediction handling by calculating training predictions (y_train_pred) before denormalization and automatically configuring datetime indices for time-series operations.
2025-12-18 17:05:10 -03:00
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
Bruno Domingues
c7f44a2423
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
Kou-Kinoshita
a80d65d7ba
SIENTIAPDE-1321: Added equation related methods
2025-10-24 08:03:27 -03:00
Bruno Domingues
94697215aa
SIENTIAPDE-1241: Refactor: Improve documentation, exception handling, and configuration in model manager. This commit enhances clarity and robustness by adding detailed docstrings to methods, standardizing exception handling with custom types, and simplifying MLflow configuration.
2025-10-22 21:48:12 -03:00
Bruno Domingues
5789a13023
SIENTIAPDE-1241: refactor train_model workflow due to I/O errors.
2025-10-22 15:37:56 -03:00
Bruno Domingues
56a21a16da
SIENTIAPDE-1255: Integrate sientia-mlops-library into model-manager, adding model serving, reporting, and updated model definitions.
2025-10-20 16:55:52 -03:00
Bruno Domingues
305a39b44c
SIENTIAPDE-1253: Implement business rule validation for training parameters. Adds a validate_business_rules method to the TrainModelParams class to enforce constraints on training parameters, improving data integrity and preventing errors. Also updates tests to include target variable in variable columns and adds tests for business rule validations.
2025-10-15 16:16:09 -03:00
Bruno Domingues
8ea98360c3
SIENTIAPDE-1253: Refactor training workflow and activities to raise exceptions on failure
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This commit refactors the training workflow and associated activities to raise exceptions on failure instead of returning success/failure dictionaries. This allows the Temporal workflow to handle errors more effectively and ensures that the workflow stops when a critical error occurs.
Key changes:
- The train_model workflow is introduced to orchestrate the entire training process, including parameter validation, data download, model training, and model saving.
- The validate_train_params activity is added to validate and convert training parameters.
- The train_model and save_model activities are updated to raise exceptions on failure.
- The ExperimentStatus enum is updated to include a new status for orchestrator validation errors.
- The tests are updated to reflect the new exception-based error handling.
- The activities now return the TrainModelResult directly instead of a dictionary.
2025-10-15 15:19:29 -03:00
Bruno Domingues
94e11df803
SIENTIAPDE-1252: Remove experiment_description from TrainModelParams and related tests.
2025-10-09 17:48:43 -03:00
Bruno Domingues
04c94efd98
SIENTIAPDE-1249: Refactor TrainModelParams to use dataclass and add from_dict method for validation, remove no-cache-dir from pip install in quality gate workflow, and rename X_train/X_test to x_train/x_test in TrainModelResult.
2025-10-06 19:11:02 -03:00
Bruno Domingues
d8583cdae7
SIENTIAPDE-1249: Implement data models for Model Manager and add unit tests. This commit introduces data transfer objects (DTOs) and model classes for experiment status, training parameters, and training results, along with corresponding unit tests to ensure their correct behavior.
2025-10-06 16:49:18 -03:00
