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ML engineering pipelines

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

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How to Impute Missing Values with Mean in Python

Replace None values in a list with the mean of the existing values using Python's statistics module.

import statistics
from statistics import mean


def impute_mean(values):
    """Replace None with the mean of the non-None values."""
    # Filter out None to compute the mean of existing values
    valid = [v for v in values if v is not None]
    if not valid:
        return values  # nothing to impute if all are Non…

14 0 Open

ML engineering pipelines easy

How to Mock train_test_split in Python for Unit Testing

Build a lightweight mock of sklearn's train_test_split to unit test ML pipeline code without needing the full library or deterministic random state.

train_test_split mock unit-testing

Python

import numpy as np
from sklearn.model_selection import train_test_split
from unittest.mock import patch

def mock_train_test_split(X, y, test_size=0.25, random_state=None, **kwargs):
    """A simple mock implementation of train_test_split."""
    n_samples = len(X)
    n_test = int(n_samples * test_size)
    n_train =…

12 0 Open

ML engineering pipelines easy

How to Save and Load PyTorch Model State Dict in Python

This code demonstrates how to save a PyTorch model's state dict to a file and load it back into a new model instance, verifying weights match.

pytorch state-dict model

Python

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(4, 8)
        self.fc2 = nn.Linear(8, 2)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

if __name__ == "__main__":
    model = Simp…

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