Reference library

ML engineering pipelines

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

3 matches

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 medium

How to Stage ML Model Workflows with Python Classes

Defines a Stage class to model ML pipeline stages with variants and mocks, printing grammar for Model, Staging, and Production stages.

ml-pipelines stages model-deployment

Python

class Stage:
    def __init__(self, name):
        self.name = name
        self.mocks = []
        self.variants = []

    def add_mock(self, mock_name):
        self.mocks.append(mock_name)

    def add_variant(self, variant_name, productions=()):
        self.variants.append((variant_name, list(productions)))

    …

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 …

14 0 Open

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