ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
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.
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(…How to Build a Mock Offline Feature Store in Python
Build an in-memory mock of an offline feature store with a dict-based FeatureStore class for storing and retrieving ML features by entity ID.
from datetime import datetime
from collections import defaultdict
class FeatureStore:
"""Simple in-memory mock of an offline feature store."""
def __init__(self):
self._features = defaultdict(dict)
def ingest(self, entity_id, feature_name, value, timestamp=None):
ts = timestamp or datet…Browse by section
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ML engineering pipelines — Python code examples
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This page collects ml engineering pipelines snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.