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

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

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

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

Hint

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

Practice

1. What does reproducibility in machine learning primarily ensure?

easy

A. The same steps produce the same results every time

B. The model trains faster on new data

C. The model uses less memory during training

D. The model automatically improves accuracy over time

Solution

1. Step 1: Understand reproducibility meaning

   Reproducibility means repeating the same process and getting the same results.

2. Step 2: Identify what reproducibility guarantees

   It guarantees consistent results, not speed, memory, or automatic improvement.

3. Final Answer:

   The same steps produce the same results every time -> Option A

4. Quick Check:

   Reproducibility = consistent results [OK]

Hint: Reproducibility means repeat and get same results [OK]

Common Mistakes:

• Confusing reproducibility with performance improvements
• Thinking reproducibility means automatic model updates
• Assuming reproducibility reduces resource use

2. Which practice helps ensure reproducibility in ML experiments?

easy

A. Skipping data preprocessing steps

B. Increasing batch size randomly

C. Using random seeds to fix randomness

D. Changing model architecture each run

Solution

1. Step 1: Identify reproducibility techniques

   Fixing randomness with seeds ensures the same random choices each run.

2. Step 2: Evaluate options for reproducibility

   Changing batch size, model, or skipping steps breaks reproducibility.

3. Final Answer:

   Using random seeds to fix randomness -> Option C

4. Quick Check:

   Random seeds fix randomness [OK]

Hint: Fix randomness with seeds for reproducibility [OK]

Common Mistakes:

• Thinking changing model each run helps reproducibility
• Ignoring the role of data preprocessing
• Assuming random batch sizes improve reproducibility

3. Given this Python snippet for setting a random seed:

import random
random.seed(42)
print(random.randint(1, 10))

What will be the output every time you run it?

medium

A. The number 2 every time

B. A different random number between 1 and 10 each run

C. The number 10 every time

D. An error because seed is not set correctly

Solution

1. Step 1: Understand random.seed(42)

   Setting seed fixes the random number sequence to be repeatable.

2. Step 2: Check random.randint(1, 10) with seed 42

   With seed 42, random.randint(1, 10) returns 2 every time.

3. Final Answer:

   The number 2 every time -> Option A

4. Quick Check:

   Seed 42 fixes output to 2 [OK]

Hint: Seed fixes random output to same number [OK]

Common Mistakes:

• Expecting different numbers each run despite seed
• Assuming seed causes errors
• Guessing max or min number instead of actual output

4. You run an ML experiment but get different results each time. Which fix will improve reproducibility?

medium

A. Remove version control from code

B. Disable containerization tools

C. Use different datasets each run

D. Set fixed random seeds in all libraries

Solution

1. Step 1: Identify cause of varying results

   Randomness without fixed seeds causes different results each run.

2. Step 2: Choose fix to ensure reproducibility

   Setting fixed seeds in all libraries ensures consistent randomness and results.

3. Final Answer:

   Set fixed random seeds in all libraries -> Option D

4. Quick Check:

   Fixed seeds improve reproducibility [OK]

Hint: Fix randomness by setting seeds everywhere [OK]

Common Mistakes:

• Removing version control thinking it helps
• Changing datasets each run breaks reproducibility
• Disabling containers reduces environment consistency

5. Which combination of practices best builds trust through reproducibility in ML?

hard

A. Training on different data splits without logging

B. Using random seeds, version control, and containerization

C. Changing hyperparameters randomly each run

D. Ignoring environment setup and dependencies

Solution

1. Step 1: Identify key reproducibility practices

   Random seeds fix randomness, version control tracks code, containers fix environment.

2. Step 2: Evaluate options for trust-building

   Only Using random seeds, version control, and containerization combines all these to ensure consistent, repeatable results.

3. Final Answer:

   Using random seeds, version control, and containerization -> Option B

4. Quick Check:

   Seeds + version control + containers = trust [OK]

Hint: Combine seeds, version control, containers for trust [OK]

Common Mistakes:

• Randomly changing hyperparameters breaks reproducibility
• Skipping logs loses experiment traceability
• Ignoring environment causes inconsistent runs