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ML engineering pipelines

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

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Champion Challenger Deployment Mock in Python

Simulates an A/B champion-challenger ML deployment workflow — comparing two mock model accuracies and deciding which to promote to production.

ml deployment champion-challenger

Python

import random
import time

class ModelMocker:
    def __init__(self, name="Model", accuracy=0.85):
        self.name = name
        self.accuracy = accuracy

    def predict(self, data):
        """Simulate prediction with some randomness."""
        time.sleep(0.005)  # simulate compute time
        return 1 if rando…

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 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 easy

How to Trigger Model Retraining on Drift in Python

Automatically detects accuracy drift in a mock ML model and triggers retraining when performance falls below a threshold.

ml drift-detection retraining

Python

import random
import time

class MockModel:
    def __init__(self, name):
        self.name = name
        self.accuracy = 0.85
        self.version = 1

    def train(self, data_size):
        # Simulate training time and accuracy improvement
        time.sleep(0.1)
        drift = random.uniform(-0.02, 0.02)
       …

16 0 Open

ML engineering pipelines easy

Load CSV Training Data Without Pandas in Python

This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.

csv data-loading standard-library

Python

import csv
from pathlib import Path

def load_csv(path):
    """Load CSV file into list of dicts without pandas."""
    rows = []
    with open(path, newline='', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            rows.append(dict(row))
    return rows

if __name__ == "__m…

13 0 Open

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