Reference library

ML engineering pipelines

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

4 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…

14 0 Open

ML engineering pipelines easy

How to Create a Mock Metaflow Flow in Python

Build a minimal Metaflow flow with two sequential steps that pass data between them using instance attributes.

metaflow ml-pipelines workflow

Python

from metaflow import FlowSpec, step, current


class MockFlow(FlowSpec):
    """A minimal Metaflow flow to demonstrate basic steps and branching."""

    @step
    def start(self):
        self.category = "mock"
        print(f"Start step for {self.category} flow")
        self.next(self.process)

    @step
    def pr…

15 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 medium

Mock a Flyte ML workflow in Python

Build a lightweight mock of a Flyte ML pipeline with dataclasses and a simple execution loop that passes outputs between tasks.

flyte ml-pipeline dataclass

Python

from dataclasses import dataclass, field
from typing import List, Dict, Optional
import time


@dataclass
class FlyteTask:
    name: str
    inputs: Dict = field(default_factory=dict)
    outputs: Dict = field(default_factory=dict)

    def run(self) -> Dict:
        time.sleep(0.1)  # simulate work
        return sel…

16 0 Open

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