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ML engineering pipelines

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

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Detect Concept Drift in Python with a Simple Statistical Test

Detect concept drift by comparing the mean of recent data against a reference distribution using a z-score-like threshold.

concept drift statistics ml monitoring

Python

import random
import statistics

def detect_drift(recent, reference, threshold=1.5):
    ref_mean = statistics.mean(reference)
    ref_std = statistics.stdev(reference)
    
    recent_mean = statistics.mean(recent)
    drift_score = abs(recent_mean - ref_mean) / (ref_std if ref_std > 0 else 1)
    
    drifted = drif…

15 0 Open

ML engineering pipelines easy

How to Impute Missing Values with Mean in Python

Replace None values in a list with the mean of the existing values using Python's statistics module.

imputation missing-data statistics

Python

import statistics
from statistics import mean


def impute_mean(values):
    """Replace None with the mean of the non-None values."""
    # Filter out None to compute the mean of existing values
    valid = [v for v in values if v is not None]
    if not valid:
        return values  # nothing to impute if all are Non…

14 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 easy

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 =…

12 0 Open

ML engineering pipelines easy

How to Save and Load PyTorch Model State Dict in Python

This code demonstrates how to save a PyTorch model's state dict to a file and load it back into a new model instance, verifying weights match.

pytorch state-dict model

Python

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(4, 8)
        self.fc2 = nn.Linear(8, 2)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

if __name__ == "__main__":
    model = Simp…

13 0 Open

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