Reference library

ML engineering pipelines

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

3 matches

How to Create a Mock ONNX Model in Python

Build and export a minimal mock ONNX model with a Reshape and Gemm layer using the onnx helper API.

import onnx
import numpy as np
from onnx import helper, TensorProto

def create_mock_model():
    # Define input and output tensors
    input_tensor = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224])
    output_tensor = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10])

   …

16 0 Open

ML engineering pipelines medium

How to Mock Kedro Pipeline Nodes in Python

Create a modular Kedro pipeline with node functions, namespacing, and input/output mapping to mock pipeline execution locally.

kedro pipeline modular

Python

from kedro.pipeline import Pipeline, node
from kedro.pipeline.modular_pipeline import pipeline as modular_pipeline


def preprocess(data: list) -> list:
    """Clean data by removing None values."""
    return [item for item in data if item is not None]


def transform(data: list) -> list:
    """Add 1 to each numeric…

15 0 Open

ML engineering pipelines medium

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.

kubeflow pipelines mlops

Python

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…

14 0 Open

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