Machine Learning Pipelines

So far, we have been performing machine learning steps one by one. We cleaned data, scaled features, trained models, and tuned hyperparameters manually.

While this approach is useful for learning, real-world machine learning systems need something more organized, repeatable, and less error-prone.

This lesson introduces ML Pipelines, a powerful concept that connects all steps into a single automated workflow.

What Is an ML Pipeline?

An ML Pipeline is a structured sequence of steps that transforms raw data into predictions.

Each step performs a specific task, such as scaling, feature selection, or model training.

Once defined, the pipeline behaves like a single model.

This makes machine learning workflows cleaner, safer, and easier to maintain.

Why Pipelines Are Important

In earlier lessons, we manually applied preprocessing before training the model.

This creates a serious risk: training and testing data may not be processed in exactly the same way.

Pipelines solve this problem by enforcing consistent transformations during both training and prediction.

Dataset Continuity

We continue using the same dataset introduced earlier:

Dataplexa ML Housing & Customer Dataset

Using a single dataset throughout the course helps you understand how real ML systems evolve step by step.

Preparing the Dataset

We start by loading the dataset and separating features from the target variable.

import pandas as pd

df = pd.read_csv("dataplexa_ml_housing_customer_dataset.csv")

X = df.drop("loan_approved", axis=1)
y = df["loan_approved"]

Creating a Pipeline

In this example, we create a pipeline that performs feature scaling followed by logistic regression.

The order matters. Each step feeds its output to the next step.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([
    ("scaler", StandardScaler()),
    ("model", LogisticRegression(max_iter=1000))
])

Training the Pipeline

The pipeline is trained just like a normal model.

Internally, it applies scaling first, then fits the classifier.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

pipeline.fit(X_train, y_train)

Evaluating the Pipeline

Evaluation is simple and safe.

The same preprocessing is automatically applied to the test data.

pipeline.score(X_test, y_test)

Combining Pipelines with Hyperparameter Tuning

One of the biggest advantages of pipelines is their compatibility with Grid Search and Random Search.

We can tune model parameters without breaking the workflow.

from sklearn.model_selection import GridSearchCV

param_grid = {
    "model__C": [0.1, 1, 10]
}

grid = GridSearchCV(
    pipeline,
    param_grid,
    cv=5,
    scoring="accuracy"
)

grid.fit(X_train, y_train)

Real-World Example

In production systems, data arrives continuously from different sources.

Pipelines ensure that every incoming record is processed exactly like training data.

This prevents silent bugs that can destroy model performance.

Mini Practice

Add another preprocessing step such as feature selection and observe how pipeline structure changes.

Exercises

Quick Quiz

In the next lesson, we move from model building to real-world usage by learning Model Deployment.