Why reproducibility builds trust in ML in MLOps - See It in Action

Why reproducibility builds trust in ML

📖 Scenario: You are working as a machine learning engineer. Your team wants to make sure that the machine learning model results can be trusted by everyone. To do this, you will create a simple example that shows how reproducibility helps build trust in ML results.

🎯 Goal: Build a small Python script that simulates training a model with random data but uses a fixed random seed to ensure the results are the same every time. This will demonstrate how reproducibility works in ML.

📋 What You'll Learn

Create a list of random numbers simulating model accuracy scores

Add a fixed random seed to control randomness

Use a loop to generate multiple accuracy scores

Print the list of accuracy scores to show reproducibility

💡 Why This Matters

🌍 Real World

In real machine learning projects, reproducibility ensures that models behave consistently and results can be trusted by data scientists and stakeholders.

💼 Career

Understanding reproducibility is key for ML engineers and data scientists to build reliable models and collaborate effectively in teams.

1

Create a list to hold accuracy scores

Create an empty list called accuracy_scores to store model accuracy values.

MLOps

# Create an empty list called accuracy_scores
# Your code here

Need a hint?

Use square brackets [] to create an empty list.

2

Set a fixed random seed

Import the random module and set the random seed to 42 using random.seed(42).

MLOps

accuracy_scores = []
# Import random and set seed to 42
# Your code here

Need a hint?

Use import random at the top and then random.seed(42) to fix randomness.

3

Generate accuracy scores using a loop

Use a for loop with variable i in range(5) to generate 5 random accuracy scores between 80 and 100 using random.randint(80, 100). Append each score to accuracy_scores.

MLOps

import random
accuracy_scores = []
random.seed(42)
# Use a for loop to generate 5 random accuracy scores and append to accuracy_scores
# Your code here

Need a hint?

Use for i in range(5): and inside the loop generate a random integer and append it.

4

Print the accuracy scores

Write a print statement to display the accuracy_scores list.

MLOps

import random
accuracy_scores = []
random.seed(42)
for i in range(5):
    score = random.randint(80, 100)
    accuracy_scores.append(score)
# Print the accuracy_scores list
# Your code here

Need a hint?

Use print(accuracy_scores) to show the list of scores.