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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 Compute a Confusion Matrix in Python

Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.

confusion-matrix classification ml-metrics

Python

from collections import defaultdict

def compute_confusion_matrix(y_true, y_pred, labels):
    """Compute confusion matrix using Python dicts and nested lists."""
    label_index = {label: i for i, label in enumerate(labels)}
    matrix = [[0] * len(labels) for _ in range(len(labels))]
    
    for true, pred in zip(y…

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ML engineering pipelines easy

How to Run Batch Predictions with a Mock Model in Python

Build a lightweight mock model class and run predictions across a batch of samples, returning results as a plain Python list.

numpy batch ml

Python

import numpy as np

class MockModel:
    def __init__(self, weights):
        self.weights = np.array(weights)

    def predict(self, X):
        return X @ self.weights

def predict_batch(model, batch):
    """Run predictions for a batch of samples and return results as a list."""
    return model.predict(np.array(ba…

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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

One Hot Encode Categories in Python

Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.

one-hot encoding categorical numpy

Python

import numpy as np

categories = ["red", "green", "blue", "red", "blue", "green", "red"]

unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}

one_hot = []
for cat in categories:
    row = [0] * len(unique)
    row[lookup[cat]] = 1
    one_hot.append(row)

print("Categories:", categories…

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