Reference library

ML engineering pipelines

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

3 matches

How to Stage ML Model Workflows with Python Classes

Defines a Stage class to model ML pipeline stages with variants and mocks, printing grammar for Model, Staging, and Production stages.

ml-pipelines stages model-deployment

Python

class Stage:
    def __init__(self, name):
        self.name = name
        self.mocks = []
        self.variants = []

    def add_mock(self, mock_name):
        self.mocks.append(mock_name)

    def add_variant(self, variant_name, productions=()):
        self.variants.append((variant_name, list(productions)))

    …

12 0 Open

ML engineering pipelines medium

How to Train a Gradient Boosting Regressor in Python

Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.

sklearn gradient-boosting regression

Python

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

def train_gradient_boosting_mock():
    # Toy regression dataset
    np.random.seed(42)
    X = np.random.rand(100, 3) * 10
    y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…

13 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

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