A/B testing model versions in MLOps - Step-by-Step Execution

Process Flow - A/B testing model versions

Deploy Model Version A

↓

Deploy Model Version B

↓

Route Traffic Split

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Users get A

↓

Collect Metrics A

↓

Compare Performance

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Choose Best Model

This flow shows deploying two model versions, splitting user traffic between them, collecting performance data, and then choosing the better model.

Execution Sample

MLOps

deploy_model('v1')
deploy_model('v2')
route_traffic({'v1': 50, 'v2': 50})
collect_metrics()
compare_metrics()
choose_best_model()

This code deploys two model versions, splits traffic evenly, collects performance data, compares results, and selects the best model.

Process Table

Step	Action	Details	Result
1	Deploy Model Version A	Model v1 deployed to production	Model v1 ready
2	Deploy Model Version B	Model v2 deployed to production	Model v2 ready
3	Route Traffic Split	50% users to v1, 50% users to v2	Traffic split established
4	Users get Predictions	Users receive predictions from assigned model	Predictions served
5	Collect Metrics	Gather accuracy and latency for v1 and v2	Metrics collected: v1=0.85 acc, v2=0.88 acc
6	Compare Performance	Compare accuracy and latency of v1 vs v2	v2 performs better
7	Choose Best Model	Select model with better metrics	Model v2 chosen for full traffic
8	End	A/B test complete	Traffic routed 100% to v2

💡 A/B test ends after comparing metrics and selecting the best model version

Status Tracker

Variable	Start	After Step 3	After Step 5	After Step 7	Final
model_v1_status	not deployed	deployed	deployed	deployed	deployed
model_v2_status	not deployed	deployed	deployed	deployed	deployed
traffic_split	none	50% v1 / 50% v2	50% v1 / 50% v2	100% v2	100% v2
metrics_v1	none	none	accuracy=0.85, latency=100ms	accuracy=0.85, latency=100ms	accuracy=0.85, latency=100ms
metrics_v2	none	none	accuracy=0.88, latency=110ms	accuracy=0.88, latency=110ms	accuracy=0.88, latency=110ms
chosen_model	none	none	none	v2	v2

Key Moments - 3 Insights

Why do we split traffic between two model versions instead of switching all users at once?

Splitting traffic (see Step 3 in execution_table) lets us compare real user responses to both models safely without risking all users on a potentially worse model.

How do we decide which model is better?

We compare collected metrics like accuracy and latency (Step 6). The model with better performance metrics is chosen (Step 7).

What happens to the traffic after choosing the best model?

After selecting the best model (Step 7), all user traffic is routed to that model (Step 8), ending the A/B test.

Visual Quiz - 3 Questions

Look at the execution_table at Step 3. What is the traffic split between model versions?

A 100% to model v2

B 100% to model v1

C 50% to model v1 and 50% to model v2

D Traffic not split yet

💡 Hint

Check the 'Details' column in Step 3 of the execution_table.

According to variable_tracker, what is the accuracy of model v2 after Step 5?

A 0.88

B 0.85

C Not collected yet

D 1.00

💡 Hint

Look at the 'metrics_v2' row under 'After Step 5' in variable_tracker.

If model v1 had better accuracy than v2, what would change in the execution_table at Step 7?

A Traffic split would remain 50/50

B Model v1 would be chosen for full traffic

C Model v2 would still be chosen

D Test would end without choosing a model

💡 Hint

Step 7 shows which model is chosen based on performance comparison in Step 6.

Concept Snapshot

A/B testing model versions:
- Deploy two model versions simultaneously.
- Split user traffic between them (e.g., 50/50).
- Collect performance metrics (accuracy, latency).
- Compare metrics to find better model.
- Route all traffic to best model after test.

Full Transcript

A/B testing model versions means running two versions of a machine learning model at the same time. First, both models are deployed. Then, user traffic is split evenly so some users get predictions from model version A and others from version B. While users interact, the system collects performance data like accuracy and response time for each model. After enough data is collected, the models are compared. The one with better performance is chosen, and all user traffic is routed to that model. This process helps safely find the best model without risking all users on an untested version.