Reference library

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…

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ML engineering pipelines easy

How to Mock Shadow Mode Inference in Python

Simulates running multiple candidate models in shadow mode by adding randomized delays and returning their outputs alongside a primary model's output.

ml-pipeline shadow-mode simulation

Python

import random
import time


def shadow_mode_inference(candidates, mock_delay=0.1):
    """
    Simulates running multiple candidate models in 'shadow mode'
    by adding tiny randomized delays and returning their outputs
    alongside the primary model's output.
    """
    primary_output = "primary: answer"
    shado…

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ML engineering pipelines easy

How to Simulate an Airflow ML Pipeline in Python

Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.

airflow ml pipeline

Python

from datetime import datetime, timedelta
import time


class MLPipeline:
    def __init__(self, pipeline_name):
        self.pipeline_name = pipeline_name
        self.steps = []

    def add_step(self, step_name, duration_seconds):
        self.steps.append({"name": step_name, "duration": duration_seconds})

    def …

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