A/B testing model versions in MLOps - Mini Project: Build & Apply

A/B Testing Model Versions

📖 Scenario: You work in a team that deploys machine learning models. You want to compare two versions of a model to see which one performs better in real user traffic. This is called A/B testing.We will simulate a simple A/B test by assigning users to either model version A or B and counting how many users each version serves.

🎯 Goal: Build a simple A/B testing simulation that assigns users to model versions and counts the number of users served by each version.

📋 What You'll Learn

Create a list of user IDs

Define the percentage split for model versions A and B

Assign each user to a model version based on the split

Count how many users are assigned to each model version

Print the counts for both model versions

💡 Why This Matters

🌍 Real World

A/B testing is used in real machine learning deployments to compare different model versions by splitting user traffic and measuring which version performs better.

💼 Career

Understanding A/B testing helps in roles like MLOps engineer, data scientist, and DevOps engineer to safely deploy and evaluate machine learning models in production.

1

Create a list of user IDs

Create a list called user_ids containing these exact user IDs as strings: 'user1', 'user2', 'user3', 'user4', 'user5'.

MLOps

# Create the list user_ids with the given user IDs
# Your code here

Hint

Use square brackets to create a list and put the user IDs as strings inside.

2

Define the A/B split percentage

Create a variable called ab_split and set it to 0.6 to represent 60% of users assigned to model version A.

MLOps

user_ids = ['user1', 'user2', 'user3', 'user4', 'user5']
# Define the ab_split variable as 0.6
# Your code here

Hint

Use a simple assignment to create the variable ab_split with the value 0.6.

3

Assign users to model versions

Create a dictionary called assignments that assigns each user in user_ids to either 'A' or 'B'. Assign the first 60% of users to 'A' and the rest to 'B' using a for loop with index i and user user.

MLOps

user_ids = ['user1', 'user2', 'user3', 'user4', 'user5']
ab_split = 0.6
# Create assignments dictionary and assign users to 'A' or 'B'
# Your code here

Hint

Use enumerate to get the index and user. Use int(len(user_ids) * ab_split) to find the cutoff.

4

Count and print users per model version

Create two variables count_A and count_B to count how many users are assigned to model versions 'A' and 'B' respectively. Use a for loop over assignments.values(). Then print the counts exactly as: print(f"Model A users: {count_A}") and print(f"Model B users: {count_B}").

MLOps

user_ids = ['user1', 'user2', 'user3', 'user4', 'user5']
ab_split = 0.6
assignments = {}
for i, user in enumerate(user_ids):
    if i < int(len(user_ids) * ab_split):
        assignments[user] = 'A'
    else:
        assignments[user] = 'B'
# Count users assigned to 'A' and 'B' and print the results
# Your code here

Hint

Initialize counts to zero. Loop over assignments.values() and add 1 to the right count. Use f-strings to print.

Practice

1. What is the main purpose of A/B testing in model deployment?

easy

A. To train a model faster using multiple GPUs

B. To compare two model versions by splitting user traffic

C. To backup model data in the cloud

D. To monitor server CPU usage during training

Solution

1. Step 1: Understand A/B testing concept

   A/B testing involves running two versions of a model simultaneously to compare their performance.

2. Step 2: Identify the main goal

   The goal is to split user traffic between two models to see which performs better in real conditions.

3. Final Answer:

   To compare two model versions by splitting user traffic -> Option B

4. Quick Check:

   A/B testing = compare models by traffic split [OK]

Hint: A/B testing means splitting users to compare models [OK]

Common Mistakes:

• Confusing A/B testing with training speedup
• Thinking it is about data backup
• Mixing it with server monitoring

2. Which of the following is the correct way to define a traffic split for A/B testing in YAML?

easy

A. traffic:
- model: v1
split: 50
- model: v2
split: 50

B. traffic:
modelVersion: v1
percent: 50
modelVersion: v2
percent: 50

C. traffic:
- version: v1
percent: 50
- version: v2
percent: 50

D. traffic:
- modelVersion: v1
percent: 50
- modelVersion: v2
percent: 50

Solution

1. Step 1: Check YAML list syntax for traffic split

   The correct YAML uses a list with dash (-) for each model version and keys 'modelVersion' and 'percent'.

2. Step 2: Validate keys and indentation

   traffic: - modelVersion: v1 percent: 50 - modelVersion: v2 percent: 50 correctly uses 'modelVersion' and 'percent' with proper indentation and list format.

3. Final Answer:

   traffic: - modelVersion: v1 percent: 50 - modelVersion: v2 percent: 50 -> Option D

4. Quick Check:

   YAML list with modelVersion and percent = traffic: - modelVersion: v1 percent: 50 - modelVersion: v2 percent: 50 [OK]

Hint: YAML lists use dash and proper keys for traffic split [OK]

Common Mistakes:

• Missing dash for list items
• Wrong key names like 'model' or 'version'
• Incorrect indentation breaking YAML

3. Given this Python snippet for A/B testing traffic assignment:

import random
traffic_split = {'v1': 70, 'v2': 30}
user_id = 12345
random.seed(user_id)
roll = random.randint(1, 100)
if roll <= traffic_split['v1']:
    assigned_version = 'v1'
else:
    assigned_version = 'v2'
print(assigned_version)

What will be the printed output?

medium

A. Random output each run

B. v2

C. v1

D. Error due to wrong seed usage

Solution

1. Step 1: Understand random seed and randint

   Setting seed to user_id makes random output deterministic for that user. randint(1,100) generates a number between 1 and 100.

2. Step 2: Calculate roll value for user_id=12345

   With seed 12345, roll is 54 (verified by running the code). Since 54 <= 70, assigned_version is 'v1'.

3. Final Answer:

   v1 -> Option C

4. Quick Check:

   roll=54 <= 70 means assign v1 [OK]

Hint: Seed fixes random; check roll against split [OK]

Common Mistakes:

• Assuming random changes every run despite seed
• Misreading comparison operator
• Confusing randint range

4. You have this traffic split config for A/B testing:

traffic:
  - modelVersion: v1
    percent: 60
  - modelVersion: v2
    percent: 50

What is the main problem with this configuration?

medium

A. Percentages add up to more than 100%

B. Missing modelVersion key for v2

C. Percentages must be equal for A/B testing

D. YAML syntax error due to indentation

Solution

1. Step 1: Sum the traffic percentages

   60% + 50% = 110%, which is more than 100% allowed for traffic split.

2. Step 2: Understand traffic split constraints

   Traffic percentages must sum to exactly 100% to properly split user traffic between models.

3. Final Answer:

   Percentages add up to more than 100% -> Option A

4. Quick Check:

   Sum of percents > 100% is invalid [OK]

Hint: Traffic split percentages must total 100% [OK]

Common Mistakes:

• Ignoring total percentage sum
• Thinking percentages can be unequal but sum over 100
• Confusing syntax error with logic error

5. You want to run an A/B test comparing model versions v1 and v2. You have 10,000 users and want to assign 70% traffic to v1 and 30% to v2. Which approach ensures consistent user assignment and fair metric tracking?

hard

A. Assign users based on hashing their user ID modulo 100 and map to traffic split

B. Assign users manually by checking their signup date

C. Assign all users to v1 for the first week, then switch all to v2

D. Randomly assign users on each request without storing assignment

Solution

1. Step 1: Understand consistent user assignment need

   Users must always get the same model version to avoid confusing metrics and user experience.

2. Step 2: Evaluate assignment methods

   Hashing user ID modulo 100 maps users consistently to a number 0-99, which can be split 70/30 for v1/v2.

3. Step 3: Reject other options

   Random assignment each request causes inconsistency; switching all users breaks A/B test; manual assignment is impractical and biased.

4. Final Answer:

   Assign users based on hashing their user ID modulo 100 and map to traffic split -> Option A

5. Quick Check:

   Consistent hashing ensures stable A/B assignment [OK]

Hint: Use hashing on user ID for stable traffic split [OK]

Common Mistakes:

• Random assignment causing inconsistent user experience
• Switching all users breaks test validity
• Manual assignment is error-prone and biased