Reference library

ML engineering pipelines

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

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How to Build a Mock ML Pipeline with Prefect in Python

Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.

prefect machine-learning pipeline

Python

from prefect import task, flow
from datetime import datetime


@task
def preprocess_data(raw_value: float) -> float:
    """Mock preprocessing: normalize the input value."""
    return raw_value / 100.0


@task
def train_model(features: float) -> dict:
    """Mock training: return a fake model artifact."""
    return …

12 0 Open

ML engineering pipelines easy

How to Build a Simple ML Pipeline with ZenML in Python

Build a mock machine learning pipeline with ZenML steps for data loading, training, and evaluation, and run it to print the final accuracy.

zenml ml pipeline

Python

from zenml import pipeline, step


@step
def load_data() -> dict:
    """Simulate loading data from a source."""
    return {"accuracy": 0.0, "loss": 1.0}


@step
def train_model(data: dict) -> dict:
    """Simulate training a model."""
    data["accuracy"] = 0.95
    data["loss"] = 0.1
    return data


@step
def eva…

13 0 Open

ML engineering pipelines easy

How to Evaluate Accuracy, Precision, and Recall in Python

Compute accuracy, precision, and recall for a binary classification model using scikit-learn's metrics functions.

metrics classification scikit-learn

Python

from sklearn.metrics import accuracy_score, precision_score, recall_score

if __name__ == "__main__":
    y_true = [0, 1, 1, 0, 1, 0, 1, 1]
    y_pred = [0, 1, 0, 0, 1, 0, 1, 1]

    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)

   …

13 0 Open

ML engineering pipelines medium

How to Mock ROC AUC in Python

Compute ROC AUC from scratch in Python using pairwise comparisons between positive and negative score distributions, ideal for testing ML models without sklearn.

machine-learning model-evaluation auc

Python

import random
from math import comb


def mock_roc_auc(scores, labels):
    """Compute mock ROC AUC by simulating a classifier's score distribution."""
    random.seed(42)
    n = len(labels)
    pos_scores = [scores[i] for i in range(n) if labels[i] == 1]
    neg_scores = [scores[i] for i in range(n) if labels[i] == …

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

K-Fold Cross Validation in Python: A Simple Implementation

Implements k-fold cross validation from scratch, splitting data into folds and computing MSE scores for a baseline mean-predictor model.

cross-validation ml model-evaluation

Python

import random
from statistics import mean


def cross_validation_scores(data, labels, k=5, seed=42):
    random.seed(seed)
    indices = list(range(len(data)))
    random.shuffle(indices)
    fold_size = len(indices) // k
    folds = []
    for i in range(k):
        if i == k - 1:
            folds.append(indices[i *…

16 0 Open

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