Phase 3: Production ML Practices · ~120 minutes · Python

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.

Hiring signal: Understanding of ml pipelines internals

What you will learn

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

Introduction

Type: Build Language: Python Prerequisites: Phase 2, Lesson 12 (Hyperparameter Tuning) Time: ~120 minutes

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

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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