Reproducible Training - ML Pipelines & Experiment Tracking
In the previous tutorial, “Fueling Production AI - Data Validation & Pipelines”, we established a pipeline for ingesting, validating, and transforming raw data into training-ready data artifacts (train.parquet, test.parquet) and a saved preprocessing pipeline (processor.joblib). While crucial, this is only the first half of building a production-ready ML system. The next critical challenge is training the model itself in a reproducible and trackable manner.
Simply running a training script manually or within a notebook environment quickly becomes untenable in a production setting. Common issues are:
- Lack of Reproducibility: Ensuring that retraining the model with the exact same code, data version, and hyperparameters yields the identical model artifact is difficult without automation.
- Experiment Chaos: Iterating on hyperparameters, feature sets, or model architectures generates numerous results. Without systematic tracking, comparing experiments, identifying the best performing model, or recalling the exact conditions of a successful run becomes impossible.
- Dependency Hell: The training process inherently depends on the specific version of the processed data. Manually managing this link is error-prone.
This tutorial addresses these challenges head-on. We will extend our DVC pipeline to include the model training and evaluation stages. You will learn how to structure your training code for automation, log experiment parameters and performance metrics using MLflow1. We will continue using the Bank Marketing dataset to train a model, evaluate its performance considering the business context, and version all resulting artifacts. By the end, you will have a fully reproducible, end-to-end pipeline from raw data to evaluated model that is ready for deployment.
Tutorial Goals
- Understand the challenges of model training and experiment management
- Organize data loading for training and evaluation stages
- Implement experiment tracking using MLflow
- Define and execute multi-stage ML pipelines using DVC
- Evaluate model performance considering business context and threshold tuning