Why A/B testing model versions in MLOps? - Purpose & Use Cases

The Big Idea

What if you could test new AI models live without risking your users' experience?

The Scenario

Imagine you have two versions of a machine learning model and want to see which one works better for your users. You try switching all users to one model, then later switch all to the other, watching results manually.

The Problem

This manual way is slow and risky. If the first model is bad, all users suffer. You can't compare models fairly because conditions change over time. Tracking results is confusing and error-prone.

The Solution

A/B testing model versions lets you run both models at the same time on different user groups. It automatically splits traffic, collects results, and shows which model performs best without risking all users.

Before vs After

✗ Before

deploy model_v1
wait days
deploy model_v2
wait days
compare results manually

✓ After

split traffic 50% model_v1, 50% model_v2
collect metrics automatically
analyze results in real-time

What It Enables

You can safely test and compare multiple model versions live, making smarter decisions faster and improving user experience continuously.

Real Life Example

A streaming service tests two recommendation models simultaneously on different user groups to see which one keeps viewers watching longer, then chooses the best model to serve everyone.

Key Takeaways

Manual model switching is slow and risky.

A/B testing runs models side-by-side safely.

It provides clear, fast insights to pick the best model.

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