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]'

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

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'}

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

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.

Practice

1. What does technical debt in ML systems usually mean?

easy

A. Extra documentation for ML models

B. Using the latest ML algorithms

C. Quick fixes that cause problems later

D. Adding more hardware resources

Solution

1. Step 1: Understand the meaning of technical debt

   Technical debt refers to shortcuts or quick fixes made during development that cause issues later.

2. Step 2: Relate to ML systems context

   In ML systems, this means messy code, missing tests, or poor design that slows future work.

3. Final Answer:

   Quick fixes that cause problems later -> Option C

4. Quick Check:

   Technical debt = Quick fixes causing future problems [OK]

Hint: Technical debt means quick fixes causing future issues [OK]

Common Mistakes:

• Confusing technical debt with adding features
• Thinking it means more hardware
• Assuming it is about documentation only

2. Which of the following is a sign of technical debt in ML code?

easy

A. Messy code with no tests

B. Well-documented and tested code

C. Using version control properly

D. Automated deployment pipelines

Solution

1. Step 1: Identify characteristics of technical debt

   Technical debt often shows as messy code and missing tests.

2. Step 2: Match options to these characteristics

   Messy code with no tests describes messy code with no tests, which is a clear sign of technical debt.

3. Final Answer:

   Messy code with no tests -> Option A

4. Quick Check:

   Messy code + no tests = Technical debt [OK]

Hint: Look for messy code and missing tests as debt signs [OK]

Common Mistakes:

• Choosing well-documented code as debt
• Confusing deployment pipelines with debt
• Thinking version control causes debt

3. Consider this ML pipeline code snippet:

def train_model(data):
    model = Model()
    model.train(data)
    return model

model1 = train_model(data1)
model2 = train_model(data2)

# Later code uses model1 and model2

What technical debt risk does this code have?

medium

A. Model objects are not saved for reuse

B. Duplicate training code causing maintenance issues

C. No risk, code is clean and reusable

D. Data is not validated before training

Solution

1. Step 1: Analyze the code behavior

   The function trains models but does not save them to disk or persistent storage.

2. Step 2: Identify technical debt risk

   Not saving models means retraining is needed every time, causing inefficiency and maintenance problems.

3. Final Answer:

   Model objects are not saved for reuse -> Option A

4. Quick Check:

   Models not saved = Technical debt risk [OK]

Hint: Check if models are saved to avoid retraining debt [OK]

Common Mistakes:

• Assuming code is clean without checking persistence
• Ignoring data validation as debt here
• Confusing duplicate code with saving models

4. You find this error in your ML system logs:

AttributeError: 'NoneType' object has no attribute 'predict'

Which technical debt issue is most likely causing this?

medium

A. Deployment pipeline is missing environment variables

B. Data preprocessing step failed silently

C. Training function has a syntax error

D. Model object was not properly saved or loaded

Solution

1. Step 1: Understand the error message

   The error means the model variable is None, so it was not loaded or saved correctly.

2. Step 2: Link error to technical debt

   Not saving/loading models properly is a common technical debt causing runtime failures.

3. Final Answer:

   Model object was not properly saved or loaded -> Option D

4. Quick Check:

   None model = save/load issue = Technical debt [OK]

Hint: None model means save/load problem causing debt [OK]

Common Mistakes:

• Blaming syntax errors for runtime NoneType errors
• Assuming data preprocessing caused this error
• Ignoring model persistence issues

5. You want to reduce technical debt in your ML system. Which approach best helps improve reliability and speed of updates?

hard

A. Train models faster by skipping data validation

B. Add automated tests and version control for models and code

C. Use complex code shortcuts to save development time

D. Avoid documentation to focus on coding

Solution

1. Step 1: Identify best practices to reduce technical debt

   Automated tests and version control improve code quality and track changes, reducing debt.

2. Step 2: Evaluate options for reliability and update speed

   Add automated tests and version control for models and code supports reliability and faster updates by preventing errors and managing versions.

3. Final Answer:

   Add automated tests and version control for models and code -> Option B

4. Quick Check:

   Tests + version control = Less technical debt [OK]

Hint: Tests and version control reduce technical debt fast [OK]

Common Mistakes:

• Skipping validation to save time increases debt
• Using shortcuts adds more debt
• Ignoring documentation harms maintainability