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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.…

13 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

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