Reference library

ML engineering pipelines

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

1 match
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

Browse by section

Each section groups closely related Python snippets.

ML engineering pipelines — Python code examples

What you will find here

This page collects ml engineering pipelines snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.

Samples vs tutorials and challenges

Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.

Related coding tools

Free online Python IDE Write and run Python in your browser — no install. Free Python code formatter Format with autopep8 — side-by-side PEP 8 cleanup. Python code samples Copy-ready snippets to study and run in the IDE.