Reference library

ML engineering pipelines

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

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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

ML engineering pipelines easy

StandardScaler mock in Python

A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.

scaling preprocessing machine-learning

Python

import math

class StandardScaler:
    def __init__(self):
        self.mean_ = None
        self.std_ = None

    def fit(self, X):
        n = len(X)
        self.mean_ = [sum(col) / n for col in zip(*X)]
        self.std_ = []
        for col in zip(*X):
            variance = sum((x - self.mean_[i]) ** 2 for i, x …

12 0 Open

ML engineering pipelines medium

Train Logistic Regression From Scratch in Python

Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.

logistic-regression machine-learning gradient-descent

Python

import numpy as np

# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])

# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]

# Initialize parameters
theta = np.zeros(X_b.shape[1])

# Hyperparameters
learning_rate = 0…

14 0 Open

ML engineering pipelines medium

Training Pipeline Orchestration Mock DAG in Python

Build a mock DAG orchestrator that runs ML pipeline stages in dependency order using topological sorting (Kahn's algorithm).

dag pipeline topological-sort

Python

from collections import deque
from dataclasses import dataclass, field


@dataclass
class DAGNode:
    name: str
    task: callable
    dependencies: list[str] = field(default_factory=list)


class MockDAG:
    def __init__(self, nodes: list[DAGNode]):
        self.nodes = {n.name: n for n in nodes}
        self.execu…

13 0 Open

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