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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 Mock train_test_split in Python for Unit Testing

Build a lightweight mock of sklearn's train_test_split to unit test ML pipeline code without needing the full library or deterministic random state.

train_test_split mock unit-testing

Python

import numpy as np
from sklearn.model_selection import train_test_split
from unittest.mock import patch

def mock_train_test_split(X, y, test_size=0.25, random_state=None, **kwargs):
    """A simple mock implementation of train_test_split."""
    n_samples = len(X)
    n_test = int(n_samples * test_size)
    n_train =…

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ML engineering pipelines easy

How to do feature selection with VarianceThreshold in Python

This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.

feature selection sklearn machine learning

Python

import numpy as np
from sklearn.feature_selection import VarianceThreshold

def main():
    # Mock dataset: 4 samples, 5 features
    X = np.array([
        [0.1, 0.2, 1.0, 1.0, 0.5],
        [0.2, 0.2, 0.0, 1.0, 0.4],
        [0.1, 0.2, 1.0, 1.0, 0.6],
        [0.3, 0.2, 1.0, 0.0, 0.5]
    ])

    # Select features w…

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ML engineering pipelines easy

How to ordinal encode categorical data in Python with sklearn

Convert job title categories into ordinal numeric labels using sklearn's OrdinalEncoder with explicit ordering.

ordinal-encoding sklearn categorical-data

Python

from sklearn.preprocessing import OrdinalEncoder
import numpy as np

# Mock data: small job title categories with known ordering
data = np.array([
    ["intern"],
    ["junior"],
    ["mid"],
    ["senior"],
    ["lead"]
])

# Define the ordinal order (lowest to highest)
categories = [["intern", "junior", "mid", "seni…

15 0 Open

ML engineering pipelines easy

StandardScaler mock in Python

A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.

scaling preprocessing machine-learning

Python

import math

class StandardScaler:
    def __init__(self):
        self.mean_ = None
        self.std_ = None

    def fit(self, X):
        n = len(X)
        self.mean_ = [sum(col) / n for col in zip(*X)]
        self.std_ = []
        for col in zip(*X):
            variance = sum((x - self.mean_[i]) ** 2 for i, x …

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