Champion-challenger model comparison in MLOps - Step-by-Step Execution

Process Flow - Champion-challenger model comparison

Deploy Champion Model

↓

Collect Performance Data

↓

Train Challenger Model

↓

Compare Champion vs Challenger

↓

Replace Champion

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Repeat Cycle

This flow shows how the current best model (champion) is tested against a new model (challenger). If the challenger performs better, it replaces the champion.

Execution Sample

MLOps

deploy_model('champion')
collect_metrics()
train_model('challenger')
if compare_models('champion', 'challenger'):
    deploy_model('challenger')

This code deploys the champion model, collects data, trains a challenger, compares both, and deploys the challenger if it performs better.

Process Table

Step	Action	Champion Performance	Challenger Performance	Decision	Result
1	Deploy champion model	N/A	N/A	N/A	Champion model live
2	Collect performance data	Accuracy=0.85	N/A	N/A	Data collected for champion
3	Train challenger model	Accuracy=0.85	Training...	N/A	Challenger training in progress
4	Challenger training complete	Accuracy=0.85	Accuracy=0.88	N/A	Challenger ready for comparison
5	Compare models	Accuracy=0.85	Accuracy=0.88	Challenger better?	Yes
6	Decision	Accuracy=0.85	Accuracy=0.88	Deploy challenger	Challenger deployed
7	Cycle repeats	Accuracy=0.88	N/A	N/A	New champion is challenger

💡 Challenger outperforms champion, so challenger replaces champion and cycle repeats.

Status Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	Final
champion_performance	N/A	0.85	0.85	0.85	0.88
challenger_performance	N/A	N/A	0.88	0.88	N/A
deployed_model	champion	champion	champion	challenger	challenger

Key Moments - 2 Insights

Why do we keep champion performance the same after challenger training?

Because the champion model is already deployed and its performance is fixed until replaced, as shown in steps 2 to 6 in the execution_table.

What happens if the challenger performance is not better?

The champion remains deployed and the challenger is discarded or retrained, which would be a 'No' decision in step 5, stopping replacement.

Visual Quiz - 3 Questions

Look at the execution table, what is the champion's accuracy after collecting performance data?

A 0.88

B N/A

C 0.85

D 0.80

💡 Hint

Check Step 2 in the execution_table under Champion Performance.

At which step does the challenger model get deployed?

A Step 6

B Step 2

C Step 4

D Step 7

💡 Hint

Look at the Decision and Result columns in the execution_table.

If the challenger had accuracy 0.83 instead of 0.88, what would happen at step 5?

A Challenger would replace champion

B Champion would remain deployed

C Both models would be deployed

D Training would restart

💡 Hint

Step 5 compares performances; challenger must be better to replace champion.

Concept Snapshot

Champion-challenger model comparison:
- Deploy current best model (champion).
- Collect performance data from champion.
- Train new model (challenger).
- Compare challenger vs champion performance.
- Deploy challenger if better; else keep champion.
- Repeat cycle for continuous improvement.

Full Transcript

Champion-challenger model comparison is a process where the current best model, called the champion, is deployed and monitored. Performance data is collected from the champion in real use. Meanwhile, a new model, called the challenger, is trained using fresh data. Once trained, the challenger is compared against the champion using performance metrics like accuracy. If the challenger performs better, it replaces the champion in deployment. Otherwise, the champion stays live. This cycle repeats to keep improving model quality over time.