Traditional ML Implementation: The high-level steps include loading data, feature engineering, data preprocessing, model training, and evaluation. After these steps, the best model is saved for deployment.
Using MLFlow: By introducing MLFlow, the process is packaged more efficiently. For every metric or parameter passed, the model's execution and results are automatically captured using MLFlow, eliminating the need for manual loops.
MLFlow Operations: The MLFlow process starts by loading the dataset, followed by feature engineering or data preprocessing. After defining the code and passing parameters, MLFlow automates the tracking of model parameters, metrics, and iterations without requiring manual loops.
MLFlow UI: This interface allows users to visualize tracked details, including parameters, metrics, artifacts, output files, and code versions. It also provides graphical insights. There's an 'auto logging' feature that captures all details, but users can choose to manually log specific metrics or parameters if they wish.
MLFlow Outputs: Upon completion, users can see various files like data used, the ML model, requirements, configuration files, and even pickle files if utilized. These are saved in a structured folder, ensuring that all pertinent details about the machine learning process are captured and easily accessible.

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