Reference library

ML engineering pipelines

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

4 matches

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…

13 0 Open

ML engineering pipelines easy

How to Save and Load a Mock Model with Pickle and joblib in Python

Serialize a custom machine learning model to a .joblib file with joblib.dump, reload it, and run a prediction with joblib.load.

joblib pickle model-serialization

Python

import joblib
from pathlib import Path

class MockModel:
    def __init__(self, weights):
        self.weights = weights

    def predict(self, features):
        return sum(w * f for w, f in zip(self.weights, features))


def save_model_pickle(model, filepath):
    with open(filepath, "wb") as f:
        joblib.dump(…

16 0 Open

ML engineering pipelines medium

How to mock an artifact store with local paths in Python for ML pipelines

Create a temporary local artifact store with dummy files and metadata to test ML pipeline code without real storage.

ml-pipelines mock tempfile

Python

import tempfile
from pathlib import Path
import json


def create_artifact_store_mock(base_path: Path = None):
    """Create a local artifact store mock directory structure."""
    if base_path is None:
        base_path = Path(tempfile.mkdtemp())

    store_layout = {
        "artifacts": [
            {"name": "mode…

13 0 Open

ML engineering pipelines easy

Load CSV Training Data Without Pandas in Python

This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.

csv data-loading standard-library

Python

import csv
from pathlib import Path

def load_csv(path):
    """Load CSV file into list of dicts without pandas."""
    rows = []
    with open(path, newline='', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            rows.append(dict(row))
    return rows

if __name__ == "__m…

13 0 Open

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