Champion-challenger model comparison in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the champion model from the model registry.

MLOps

champion_model = model_registry.[1]('champion')

Drag options to blanks, or click blank then click option'

Aload

Bdelete

Csave

Dregister

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' instead of 'load' will try to store a model, not retrieve it.

Using 'delete' removes the model, which is not desired here.

The load method retrieves the champion model from the registry for evaluation or deployment.

2fill in blank

medium

Complete the code to compare the challenger model's accuracy with the champion's.

MLOps

if challenger_accuracy [1] champion_accuracy:

Drag options to blanks, or click blank then click option'

A<

B==

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' would check if challenger is worse, which is not the goal.

Using '==' only checks equality, not improvement.

We check if the challenger model's accuracy is greater than the champion's to decide if it should replace the champion.

3fill in blank

hard

Fix the error in the code that registers the challenger model as the new champion.

MLOps

model_registry.[1](challenger_model, name='champion')

Drag options to blanks, or click blank then click option'

Aload

Bregister

Cdelete

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' might store the model but not update the registry name.

Using 'load' tries to retrieve a model, not register it.

The register method is used to add or update a model in the registry under a specific name.

4fill in blank

hard

Fill both blanks to create a function that evaluates model performance and decides if the challenger should replace the champion.

MLOps

def should_replace(challenger_score, champion_score):
    return challenger_score [1] champion_score and challenger_score [2] 0.8

Drag options to blanks, or click blank then click option'

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' in the first blank would reverse the logic.

Using '<=' in the second blank would allow scores below 0.8.

The function returns True if the challenger score is greater than the champion's and also meets a minimum threshold of 0.8 or higher.

5fill in blank

hard

Fill all three blanks to update the model registry only if the challenger outperforms the champion and meets quality standards.

MLOps

if challenger_metrics['accuracy'] [1] champion_metrics['accuracy'] and challenger_metrics['f1_score'] [2] 0.75:
    model_registry.[3](challenger_model, name='champion')

Drag options to blanks, or click blank then click option'

A>

B>=

Cregister

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' instead of 'register' may not update the champion label.

Using '>=' in the first blank would allow equal accuracy, not strictly better.

The code checks if the challenger accuracy is greater than the champion's, the F1 score is at least 0.75, then registers the challenger as the new champion.

Practice

1. What is the main purpose of the champion-challenger model comparison in MLOps?

easy

A. To avoid updating models once deployed

B. To deploy models without any testing

C. To manually select models based on intuition

D. To safely test new models against the current best model

Solution

1. Step 1: Understand the champion-challenger concept

   The champion-challenger approach involves comparing a new model (challenger) against the current best model (champion) to decide which performs better.

2. Step 2: Identify the purpose of this comparison

   This comparison ensures that only better or equally good models replace the champion, keeping the system improving safely.

3. Final Answer:

   To safely test new models against the current best model -> Option D

4. Quick Check:

   Champion-challenger = safe model testing [OK]

Hint: Champion tests new models safely against current best [OK]

Common Mistakes:

• Thinking models are deployed without testing
• Believing model selection is based on guesswork
• Assuming models never get updated

2. Which of the following is the correct way to describe the champion-challenger process?

easy

A. Compare challenger and champion models using consistent data and metrics

B. Only compare models based on training accuracy

C. Deploy the challenger model immediately without comparison

D. Replace champion model randomly

Solution

1. Step 1: Review the process requirements

   Champion-challenger comparison requires fair testing using the same data and metrics to ensure valid results.

2. Step 2: Evaluate the options

   Only Compare challenger and champion models using consistent data and metrics describes comparing models fairly with consistent data and metrics, which is correct.

3. Final Answer:

   Compare challenger and champion models using consistent data and metrics -> Option A

4. Quick Check:

   Fair comparison = consistent data and metrics [OK]

Hint: Always compare models with same data and metrics [OK]

Common Mistakes:

• Deploying challenger without comparison
• Using only training accuracy for comparison
• Replacing models randomly

3. Given the following scenario: The champion model has an accuracy of 85%, and the challenger model has an accuracy of 87% on the same test set. What should happen next?

medium

A. Keep the champion model because it was deployed first

B. Deploy both models simultaneously without comparison

C. Replace the champion with the challenger model

D. Discard the challenger model due to overfitting risk

Solution

1. Step 1: Compare model accuracies on the same test set

   The challenger model has higher accuracy (87%) than the champion (85%) on consistent data.

2. Step 2: Decide based on performance

   Since the challenger performs better, it should replace the champion to improve the system.

3. Final Answer:

   Replace the champion with the challenger model -> Option C

4. Quick Check:

   Higher accuracy challenger replaces champion [OK]

Hint: Higher accuracy challenger replaces champion [OK]

Common Mistakes:

• Keeping champion just because it was first
• Deploying both without comparison
• Discarding challenger without valid reason

4. You run a champion-challenger test but notice the challenger model was evaluated on different data than the champion. What is the likely issue?

medium

A. The comparison is invalid due to inconsistent data

B. The challenger model is guaranteed better

C. Champion model should be discarded immediately

D. Data difference does not affect model comparison

Solution

1. Step 1: Identify the problem with data inconsistency

   Using different data sets for champion and challenger breaks fairness in comparison.

2. Step 2: Understand the impact on results

   This inconsistency makes the comparison invalid because performance differences may be due to data, not model quality.

3. Final Answer:

   The comparison is invalid due to inconsistent data -> Option A

4. Quick Check:

   Consistent data is key for valid comparison [OK]

Hint: Different data means invalid comparison [OK]

Common Mistakes:

• Assuming challenger is better without fair test
• Discarding champion without valid reason
• Ignoring data consistency importance

5. You want to automate champion-challenger comparisons in your MLOps pipeline. Which approach ensures fair and reliable model selection?

hard

A. Deploy challenger model immediately after training

B. Use the same validation dataset and evaluation metrics for both models in an automated test

C. Compare models only on training data accuracy

D. Randomly select a model to deploy without evaluation

Solution

1. Step 1: Define automation requirements for fair comparison

   Automation must use consistent validation data and metrics to fairly evaluate both models.

2. Step 2: Evaluate options for reliability

   Only Use the same validation dataset and evaluation metrics for both models in an automated test describes a method that ensures fair, reliable, and automated champion-challenger comparison.

3. Final Answer:

   Use the same validation dataset and evaluation metrics for both models in an automated test -> Option B

4. Quick Check:

   Automation needs consistent data and metrics [OK]

Hint: Automate with same data and metrics for fairness [OK]

Common Mistakes:

• Skipping evaluation before deployment
• Using training data for comparison
• Random model deployment