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ML engineering pipelines

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

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Build a Mock Random Forest Classifier in Python

Create a simple random-forest-like classifier with random majority voting between trees, including fit, predict, and predict_proba methods.

random forest mock machine learning

Python

import random


class MockRandomForest:
    def __init__(self, n_trees=10, random_state=42):
        self.n_trees = n_trees
        self.random_state = random_state
        self.classes_ = None
        self._class_counts = None
        random.seed(random_state)

    def fit(self, X, y):
        self.classes_ = sorted(…

13 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 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 easy

How to do feature selection with VarianceThreshold in Python

This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.

feature selection sklearn machine learning

Python

import numpy as np
from sklearn.feature_selection import VarianceThreshold

def main():
    # Mock dataset: 4 samples, 5 features
    X = np.array([
        [0.1, 0.2, 1.0, 1.0, 0.5],
        [0.2, 0.2, 0.0, 1.0, 0.4],
        [0.1, 0.2, 1.0, 1.0, 0.6],
        [0.3, 0.2, 1.0, 0.0, 0.5]
    ])

    # Select features w…

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