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.
Prerequisites: Phase 2, Lesson 12 (Hyperparameter Tuning)
#Learning Objectives
- 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
Keep going. The rest of this lesson is one step away.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.