End-to-end ML pipelines

A real ML project is far more than fitting a model. The typical end-to-end pipeline:

• Define the problem: what decision will the model support? What metric matters? What baseline exists?
• Collect and understand the data: sources, labels, quality, exploratory analysis.
• Prepare the data: clean, handle missing values, encode categories, scale, engineer features, split train/validation/test.
• Model: start with a simple baseline, then try stronger models and tune them with cross-validation.
• Evaluate: on the held-out test set, with the right metrics, plus error analysis.
• Deploy: serve predictions through an API or batch job.
• Monitor and maintain: watch for data drift and performance decay, and retrain.

Put preprocessing and model into a single pipeline object (e.g. scikit-learn's Pipeline), so exactly the same steps run during training, cross-validation and production — and so preprocessing is fitted only on training folds.