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ML engineering pipelines

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

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Build a Mock Random Forest Classifier in Python

Create a simple random-forest-like classifier with random majority voting between trees, including fit, predict, and predict_proba methods.

random forest mock machine learning

Python

import random


class MockRandomForest:
    def __init__(self, n_trees=10, random_state=42):
        self.n_trees = n_trees
        self.random_state = random_state
        self.classes_ = None
        self._class_counts = None
        random.seed(random_state)

    def fit(self, X, y):
        self.classes_ = sorted(…

13 0 Open

ML engineering pipelines easy

Create a Minimal Great Expectations Suite Mock in Python

Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.

great-expectations mock testing

Python

import json


class GreatExpectationsSuite:
    """A minimal mock of a Great Expectations suite."""

    def __init__(self, suite_name, expectations=None):
        self.suite_name = suite_name
        self.expectations = expectations or []

    def add_expectation(self, expectation_type, column=None, kwargs=None):
   …

11 0 Open

ML engineering pipelines easy

How to Load CSV Training Data in Python Without Pandas

Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.

csv ml-pipelines io-stringio

Python

import csv
from pathlib import Path


def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
    """Load CSV training data and return headers plus rows as dictionaries."""
    with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
        reader = csv.DictReader…

14 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 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 implement a canary traffic split in Python

Route incoming traffic between stable and canary model or service versions using a weight-based random split with deterministic testing.

canary traffic-split random

Python

import random


def canary_route(service_name: str, canary_weight: float = 0.2) -> str:
    """Route traffic between stable and canary versions based on weight."""
    rng = random.Random(42)  # deterministic for reproducible demo
    if rng.random() < canary_weight:
        return f"{service_name}-canary"
    return …

14 0 Open

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