ML Pipelines
A model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible. Language: Python Build an ML pipeline from scratch that chains imputation, scaling, encoding, and model training into a single reproducible object. Identify data leakage scenarios and explain how pipelines prevent them by fitting transformers only on training data. Construct a ColumnTransformer that applies different preprocessing to numeric and categorical features. Implement pipeline serialization and demonstrate that the same fitted pipeline produces identical results in training and production. You have a notebook that loads data, fills missing values with the median, scales features, trains a model, and prints accuracy. It works. You ship it. A month later, someone retrains the model and gets different results. The median was computed on the full dataset including test data (data leakage). The scaling parameters were not saved, so inference uses different statistics. The feature engineering code was copy-pasted between training and serving, and the copies diverged. A categorical column gained a new value in production that the encoder has never seen. These are not hypothetical. They are the most common reasons ML systems fail in production. Pipelines solve all of them by packaging every transformation step into a single, ordered, reproducible object. A pipeline is an ordered sequence of…
ML Pipelines: A model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.
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