ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
How to Mock a Kubeflow Pipeline in Python
Build a minimal in-memory mock of a Kubeflow pipeline DAG using dataclasses and OrderedDict to chain component functions.
from typing import Dict, Any
from dataclasses import dataclass, field
from collections import OrderedDict
@dataclass
class KubeflowPipelineMock:
"""A minimal mock of a Kubeflow pipeline DAG."""
name: str
components: OrderedDict[str, callable] = field(default_factory=OrderedDict)
def add_component(se…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.
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…Browse by section
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ML engineering pipelines — Python code examples
What you will find here
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.
Samples vs tutorials and challenges
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.