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