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.
Note: This article references the academic demonstration version of the pipeline.
Some implementation details have been simplified or removed for IP protection.
Full implementation available under commercial license.
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.
When partial steps are rerun with dvc repro, the pipeline references the same logging configuration, guaranteeing consistency of output. For extensive hyperparameter searches, scripts like dependencies/modeling/rf_optuna_trial.py record each trial’s metrics in MLflow while also appending pertinent messages to local log files. Prefect flows can capture and forward logs to the same destination for a cohesive monitoring approach.
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