Technical debt in ML systems in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main cause of technical debt in ML systems.

MLOps

technical_debt_cause = '[1]'

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

Afast_deployment

Bmodel_complexity

Csimple_code

Dcomplex_data_dependencies

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing simple_code or fast_deployment as the cause.

The main cause of technical debt in ML systems is complex data dependencies, which make maintenance harder.

2fill in blank

medium

Complete the code to show a common symptom of technical debt in ML systems.

MLOps

symptom = '[1]'

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Ahidden_feedback_loops

Bsimple_testing

Cclear_documentation

Dslow_model_training

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing slow_model_training or clear_documentation.

Hidden feedback loops are a common symptom of technical debt in ML systems, causing unexpected behavior.

3fill in blank

hard

Fix the error in the code that causes technical debt by selecting the right practice.

MLOps

def deploy_model():
    # Avoid [1] to reduce technical debt
    pass

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

Amanual_deployment

Bautomated_testing

Ccontinuous_integration

Dcode_review

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing automated_testing or continuous_integration.

Manual deployment increases technical debt by causing inconsistencies and errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that tracks model versions and their status.

MLOps

model_status = {version[1]: status for version, status in versions.items() if status [2] 'deployed'}

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A.upper()

B==

C!=

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using != instead of == for filtering.

Using .upper() converts version keys to uppercase, and == filters deployed models.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps feature names to their importance scores, filtering only positive scores.

MLOps

feature_importance = { [1]: [2] for [1], [2] in features.items() if [2] [3] 0 }

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

Afeature

Bimportance

C>

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent variable names or wrong comparison operator.

The comprehension maps each feature to its importance if the importance is greater than zero.