Reference library

ML engineering pipelines

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

27 matches

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

ML engineering pipelines easy

Model registry version mock in Python

A simple in-memory model registry that stores model versions with metadata and supports version listing and latest retrieval.

ml-engineering model-registry versioning

Python

class ModelRegistry:
    def __init__(self):
        self.models = {}

    def register(self, name, version, model_type, metrics=None):
        if name not in self.models:
            self.models[name] = []
        entry = {
            "version": version,
            "model_type": model_type,
            "metrics": m…

13 0 Open

ML engineering pipelines easy

One Hot Encode Categories in Python

Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.

one-hot encoding categorical numpy

Python

import numpy as np

categories = ["red", "green", "blue", "red", "blue", "green", "red"]

unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}

one_hot = []
for cat in categories:
    row = [0] * len(unique)
    row[lookup[cat]] = 1
    one_hot.append(row)

print("Categories:", categories…

13 0 Open

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