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ML engineering pipelines

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

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Bayesian Optimization in Python: A Simplified Mock Implementation

A toy Bayesian optimization loop with a Gaussian process prior, expected improvement acquisition, and noisy sampling to find a function's minimum.

bayesian-optimization gaussian-process hyperparameter-tuning

Python

import random
import math

class BayesianOptimizer:
    def __init__(self, noise=0.1):
        self.noise = noise
        self.observations = []
    
    def objective(self, x):
        return (math.sin(3*x) + 0.5*x) / (1 + x**2)
    
    def gaussian_process_prior(self, x1, x2, length_scale=0.5):
        return math.…

12 0 Open

ML engineering pipelines medium

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 medium

How to Build an sklearn Pipeline with ColumnTransformer in Python

A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.

sklearn pipeline columntransformer

Python

import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression

# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…

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

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