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ML engineering pipelines

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

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Compare Model A vs Model B Metrics in Python

A script that simulates and compares metrics between two ML models, showing a formatted diff table for quick insight.

import random


def compare_a_b(samples=5):
    """Mock comparison of model A vs model B predictions."""
    metrics = ["accuracy", "precision", "recall", "f1"]
    print(f"{'Metric':<12}{'Model A':>10}{'Model B':>10}{'Diff':>10}")
    print("-" * 42)

    random.seed(42)
    for metric in metrics:
        a = round(r…

14 0 Open

ML engineering pipelines easy

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…

14 0 Open

ML engineering pipelines easy

How to Evaluate Accuracy, Precision, and Recall in Python

Compute accuracy, precision, and recall for a binary classification model using scikit-learn's metrics functions.

metrics classification scikit-learn

Python

from sklearn.metrics import accuracy_score, precision_score, recall_score

if __name__ == "__main__":
    y_true = [0, 1, 1, 0, 1, 0, 1, 1]
    y_pred = [0, 1, 0, 0, 1, 0, 1, 1]

    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)

   …

13 0 Open

ML engineering pipelines easy

How to Mock MLflow log_params and log_metrics in Python

Use unittest.mock to patch MLflow's log_param and log_metric, run the training function, and verify logging calls without touching a real tracking server.

mlflow mock testing

Python

from unittest.mock import Mock, patch
import mlflow


def train_model():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("epochs", 10)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("loss", 0.05)
    return "Training completed"


if __name__ == "__main__":
    with patch("mlflow.log_par…

15 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

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