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ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

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How to Build an sklearn Pipeline with ColumnTransformer in Python

A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.

sklearn pipeline columntransformer

Python

import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression

# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…

13 0 Open

ML engineering pipelines medium

How to Mock ROC AUC in Python

Compute ROC AUC from scratch in Python using pairwise comparisons between positive and negative score distributions, ideal for testing ML models without sklearn.

machine-learning model-evaluation auc

Python

import random
from math import comb


def mock_roc_auc(scores, labels):
    """Compute mock ROC AUC by simulating a classifier's score distribution."""
    random.seed(42)
    n = len(labels)
    pos_scores = [scores[i] for i in range(n) if labels[i] == 1]
    neg_scores = [scores[i] for i in range(n) if labels[i] == …

12 0 Open

ML engineering pipelines medium

How to Train a Gradient Boosting Regressor in Python

Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.

sklearn gradient-boosting regression

Python

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

def train_gradient_boosting_mock():
    # Toy regression dataset
    np.random.seed(42)
    X = np.random.rand(100, 3) * 10
    y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…

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