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Spotlight MLflow Integration
This spotlight highlights how MLflow is integrated into a Hydra-based pipeline for reliable experiment tracking.
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Spotlight Logging for MLOps Consistency
A unified logging strategy underpins every stage of this MLOps pipeline. Hydra configuration files, such as configs/logging_utils/base.yaml define a uniform format and verbosity level, ensuring consistent output from scripts managing data ingestion, transformations, or hyperparameter optimization. By assigning a unique run ID to each pipeline execution, logs tie neatly into both DVC and MLflow runs.
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Spotlight Jinja2 Templates for Efficient Pipeline Generation
Large machine learning pipelines can suffer from repetitive edits when stage definitions expand or change.
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Spotlight Hyperparameter Tuning with Hydra, Optuna, and MLflow
This project integrates Hydra configs, Optuna optimization, and MLflow tracking to streamline hyperparameter tuning.
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Spotlight Feature Engineering for Reproducibility and Scalability
A strong feature engineering pipeline should maintain clean separation between data ingestion, cleaning, and transformation steps.