Why models degrade in production in MLOps - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main cause of model degradation in production.

MLOps

if data_distribution_changes == [1]:
    alert('Model performance may degrade')

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

ATrue

BFalse

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using False instead of True

Using None which is not a boolean

Model degradation often happens when the data distribution changes in production, so the condition should be True.

2fill in blank

medium

Complete the code to log model accuracy drop in production.

MLOps

if current_accuracy [1] baseline_accuracy:
    log('Accuracy dropped')

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

A>=

B!=

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using >= which means accuracy improved or stayed same

Using == which only detects equality

We want to log when current accuracy is less than baseline, indicating degradation.

3fill in blank

hard

Fix the error in the code that checks for data drift.

MLOps

if detect_data_drift() [1]:
    retrain_model()

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

Ais False

B!=

C== None

Dis True

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'is False' which means no drift

Using '== None' which is incorrect for booleans

The function detect_data_drift() returns True if drift is detected, so we check if it is True.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that tracks feature importance changes above a threshold.

MLOps

importance_changes = {feature: importance[1]old_importance for feature, importance in new_importances.items() if importance[1]old_importance [2] threshold}

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

A-

B>

C<

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '-' for difference

Using '<' which filters smaller changes

The difference is calculated by subtracting old importance from new, and we filter features where this difference is greater than the threshold.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of features with significant importance drop.

MLOps

significant_drops = { [1]: [2] for [3], importance in old_importances.items() if importance - new_importances.get([1], 0) > 0.1 }

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

Afeature

Bimportance

Dfeat

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names causing errors

Mixing up keys and values

The dictionary keys and values use 'feature' and 'importance' variables consistently to track drops.