Reference library

ML engineering pipelines

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

3 matches

How to Load, Save, and Split JSON Data in Python

Provides helper functions to load, save, and split JSON dictionary data for simple ML pipeline preprocessing.

import json
from pathlib import Path


def load_json_data(file_path):
    """Load JSON data from a file, returning an empty dict if missing."""
    path = Path(file_path)
    if path.exists():
        with path.open("r", encoding="utf-8") as f:
            return json.load(f)
    return {}


def save_json_data(data, f…

13 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 …

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