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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.
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…How to Create a Mock Metaflow Flow in Python
Build a minimal Metaflow flow with two sequential steps that pass data between them using instance attributes.
from metaflow import FlowSpec, step, current
class MockFlow(FlowSpec):
"""A minimal Metaflow flow to demonstrate basic steps and branching."""
@step
def start(self):
self.category = "mock"
print(f"Start step for {self.category} flow")
self.next(self.process)
@step
def pr…How to Generate Experiment Tracking Run IDs in Python
Generate unique experiment run IDs with timestamps and random suffixes for tracking ML pipeline executions.
import random
import string
import time
def generate_run_id(prefix="exp"):
timestamp = time.strftime("%Y%m%d_%H%M%S")
suffix = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
return f"{prefix}_{timestamp}_{suffix}"
if __name__ == "__main__":
# Simulate tracking three experiment r…How to Mock a Feature Store Online Lookup in Python
This code simulates an online feature store with single and batch retrieval methods, using a dict-backed cache and timestamps.
import random
import time
class OnlineFeatureStore:
def __init__(self):
self.features = {}
def put(self, entity_id: str, feature_name: str, value):
key = (entity_id, feature_name)
self.features[key] = (value, time.time())
def get(self, entity_id: str, feature_name: str):
…How to Simulate an Airflow ML Pipeline in Python
Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.
from datetime import datetime, timedelta
import time
class MLPipeline:
def __init__(self, pipeline_name):
self.pipeline_name = pipeline_name
self.steps = []
def add_step(self, step_name, duration_seconds):
self.steps.append({"name": step_name, "duration": duration_seconds})
def …How to implement a canary traffic split in Python
Route incoming traffic between stable and canary model or service versions using a weight-based random split with deterministic testing.
import random
def canary_route(service_name: str, canary_weight: float = 0.2) -> str:
"""Route traffic between stable and canary versions based on weight."""
rng = random.Random(42) # deterministic for reproducible demo
if rng.random() < canary_weight:
return f"{service_name}-canary"
return …How to Mock a Remote Config Fetch in Python
Simulate a remote config API response with metadata, timestamps, and mock data for testing or local development.
import json
from datetime import datetime
from typing import Any, Dict
def fetch_remote_config(mock_data: Dict[str, Any]) -> Dict[str, Any]:
"""Simulate fetching a remote config with metadata and timestamps."""
return {
"status": "success",
"source": "mock",
"fetched_at": datetime.utcn…Broadcast a Small Reference Table in Python
Simulates SQL-style broadcasting of a small lookup table against a larger fact table in memory for mockups or load tests.
import random
def broadcast_mock(target, source, columns):
result = {}
for col in columns:
if col in target and col in source:
result[col] = target[col] + [source[col][i % len(source[col])] for i in range(len(target[col]))]
elif col in target:
result[col] = target[col]
…Database indexing and query timing optimization in Python
Create SQLite indexes and time query performance to measure speedup for large table lookups in Python.
import sqlite3
import time
def time_query(db_path, query, params=()):
conn = sqlite3.connect(db_path)
conn.execute("PRAGMA journal_mode = WAL")
start = time.perf_counter()
result = conn.execute(query, params).fetchall()
elapsed = time.perf_counter() - start
conn.close()
return result, ela…Geo shard by region in Python
Maps users to database shards based on geographic region with a deterministic hash fallback.
import json
from collections import defaultdict
REGION_SHARD_MAP = {
"na": ["shard-01", "shard-02"],
"eu": ["shard-03", "shard-04", "shard-05"],
"ap": ["shard-06"],
"sa": ["shard-07", "shard-08"],
}
# user_id -> region (mock lookup)
USER_REGIONS = {
"u_1001": "na",
"u_1002": "eu",
"u_1003…How to Count Star vs Estimate Matches in Python
Count how many times 'star' and 'estimate' annotations match their actual labels in a list of mock comparison results.
def count_star_vs_estimate(mock_scores):
"""
Count the number of times 'star' wins and 'estimate' wins
from a list of mock comparison results.
Args:
mock_scores: list of tuples, each (annotation, actual)
where annotation is 'star' or 'estimate'
Returns:
dict w…How to Speed Up Column Lookups with DataFrame Index in Python
Use pandas set_index to make repeated column value lookups O(1)-style fast instead of scanning the whole DataFrame each time.
import pandas as pd
# Mock dataset with duplicate customer IDs
data = {"customer_id": [101, 102, 103, 101, 104, 102],
"order_amount": [250.0, 85.5, 300.0, 175.25, 420.0, 95.75]}
df = pd.DataFrame(data)
df = df.set_index("customer_id")
# Simulated lookup request
search_id = 102
# Fast index-based lookup (no…How to Mock a GitHub Actions Workflow in Python
Build a dataclass-based model of a GitHub Actions workflow and simulate its execution to validate steps and outputs before deployment.
import json
from dataclasses import dataclass, asdict
from typing import List, Dict, Any
@dataclass
class Step:
name: str
run: str
@dataclass
class Job:
name: str
steps: List[Step]
runs_on: str = "ubuntu-latest"
@dataclass
class Workflow:
name: str
jobs: List[Job]
def to_github_a…How to Simulate a Packer AMI Build in Python
A simple Python class that mimics a Packer AMI build lifecycle — creates a build object, transitions its state to completed, and prints a JSON snapshot.
import json
class PackerBuildMock:
def __init__(self, name, ami_id, region="us-east-1", state="pending"):
self.name = name
self.ami_id = ami_id
self.region = region
self.state = state
def build(self):
if self.state == "pending":
self.state = "completed"
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