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ML engineering pipelines

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

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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…

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ML engineering pipelines easy

How to ordinal encode categorical data in Python with sklearn

Convert job title categories into ordinal numeric labels using sklearn's OrdinalEncoder with explicit ordering.

ordinal-encoding sklearn categorical-data

Python

from sklearn.preprocessing import OrdinalEncoder
import numpy as np

# Mock data: small job title categories with known ordering
data = np.array([
    ["intern"],
    ["junior"],
    ["mid"],
    ["senior"],
    ["lead"]
])

# Define the ordinal order (lowest to highest)
categories = [["intern", "junior", "mid", "seni…

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ML engineering pipelines easy

One Hot Encode Categories in Python

Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.

one-hot encoding categorical numpy

Python

import numpy as np

categories = ["red", "green", "blue", "red", "blue", "green", "red"]

unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}

one_hot = []
for cat in categories:
    row = [0] * len(unique)
    row[lookup[cat]] = 1
    one_hot.append(row)

print("Categories:", categories…

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