Quest 9 of 15
Feature engineering & data pipelines
Production ML is mostly data: cleaning, encoding categoricals, scaling, pipeline objects, and versioned datasets with sklearn Pipeline and pandas.
Start here
This module builds on earlier parts, but every important idea is explained in context. Production ML is mostly data: cleaning, encoding categoricals, scaling, pipeline objects, and versioned datasets with sklearn Pipeline and pandas. As you read, connect each concept to the worked example and ask what a person must still decide.
Big idea
Production ML is mostly data: cleaning, encoding categoricals, scaling, pipeline objects, and versioned datasets with sklearn Pipeline and pandas.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 8
Split raw examples before learning any preprocessing statistics. Fit imputers, scalers, encoders, and feature selection on training data only, then use those fitted transformations on validation and test data.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Schema drift breaks models
A new categorical level at inference time must have a defined strategy (unknown bucket, retrain).
Deep dive 2Pipelines encode decisions
Pipeline objects document exact training-time transforms — critical for audit and reproduction.
Deep dive 3Worked example: Feature engineering & data pipelines
Production logs show a new 'UNKNOWN' city code crashing the encoder. A responsible response is: Add handle_unknown='ignore' or explicit bucket; monitor frequency; plan retrain. Correct production hygiene. Use this case to separate what the technology contributes from what people contribute. The team should compare the intended outcome with a baseline where applicable, record important assumptions, and keep a clear route to correct or stop the process.
Deep dive 4Local check for Feature engineering & data pipelines
Ask whether the examples, data, and assumptions fit your school, company, or community. Generic demos often miss local names, laws, connectivity, and languages.
Deep dive 5One pipeline from raw row to prediction
Fit imputation, encoding, and scaling on training data, then package them with the estimator for serving.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Leakage-safe pipeline
Raw row → ColumnTransformer → model → prediction; same path in train and serve.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Wrap scaler + model in one Pipeline and call predict on raw DataFrame row.
Extra context (audience, logistics, curriculum notes)
Built for: Software developers and technical learners — Python and coding required.
Formats: Learn scroll · Practice interactions · Quiz · Optional chat lab
Module 9 — Feature engineering & data pipelines
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