Automated retraining triggers in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to trigger retraining when new data arrives.

MLOps

if new_data [1] True:
    trigger_retraining()

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

A<

B==

C!=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' causes retraining not to trigger when data is present.

We check if new_data is equal to True to trigger retraining.

2fill in blank

medium

Complete the code to check if model accuracy dropped below threshold.

MLOps

if accuracy [1] threshold:
    trigger_retraining()

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

A<

B>=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>=' triggers retraining when accuracy is good.

Retraining triggers when accuracy is less than the threshold.

3fill in blank

hard

Fix the error in the retraining trigger condition.

MLOps

if model_drift [1] True:
    trigger_retraining()

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

A=

B!=

C==

Dis

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' causes syntax error.

Use '==' to compare values. '=' is assignment and causes error.

4fill in blank

hard

Fill both blanks to trigger retraining when data is new and accuracy is low.

MLOps

if [1] and [2]:
    trigger_retraining()

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

Anew_data == True

Baccuracy >= threshold

Caccuracy < threshold

Dmodel_drift == False

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy >= threshold triggers retraining incorrectly.

Retraining triggers only if new data is present and accuracy is below threshold.

5fill in blank

hard

Fill all three blanks to create a retraining trigger based on data, accuracy, and drift.

MLOps

if [1] or ([2] and [3]):
    trigger_retraining()

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

Anew_data == True

Baccuracy < threshold

Cmodel_drift == True

Daccuracy >= threshold

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy >= threshold in second blank prevents retraining when accuracy is low.

Retraining triggers if new data arrives, or if accuracy is low and model drift is detected.