ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
Compare Model A vs Model B Metrics in Python
A script that simulates and compares metrics between two ML models, showing a formatted diff table for quick insight.
import random
def compare_a_b(samples=5):
"""Mock comparison of model A vs model B predictions."""
metrics = ["accuracy", "precision", "recall", "f1"]
print(f"{'Metric':<12}{'Model A':>10}{'Model B':>10}{'Diff':>10}")
print("-" * 42)
random.seed(42)
for metric in metrics:
a = round(r…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.
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):
…How to Compute a Confusion Matrix in Python
Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.
from collections import defaultdict
def compute_confusion_matrix(y_true, y_pred, labels):
"""Compute confusion matrix using Python dicts and nested lists."""
label_index = {label: i for i, label in enumerate(labels)}
matrix = [[0] * len(labels) for _ in range(len(labels))]
for true, pred in zip(y…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.
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)
…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.
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…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.
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…Browse by section
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ML engineering pipelines — Python code examples
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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.
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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.