Reference library

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

K-Fold Cross Validation in Python: A Simple Implementation

Implements k-fold cross validation from scratch, splitting data into folds and computing MSE scores for a baseline mean-predictor model.

cross-validation ml model-evaluation

Python

import random
from statistics import mean


def cross_validation_scores(data, labels, k=5, seed=42):
    random.seed(seed)
    indices = list(range(len(data)))
    random.shuffle(indices)
    fold_size = len(indices) // k
    folds = []
    for i in range(k):
        if i == k - 1:
            folds.append(indices[i *…

16 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 …

12 0 Open

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