Technical debt in ML systems in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Technical Debt in ML Pipelines

Which of the following best describes a common source of technical debt in machine learning pipelines?

A Documenting model assumptions and limitations clearly

B Training models with the latest data and retraining regularly

C Using outdated data schemas without updating the pipeline components

D Implementing automated testing for data validation

💡 Hint

Think about what causes maintenance problems over time in ML systems.

Using outdated data schemas causes components to break or behave unexpectedly, creating technical debt.

💻 Command Output

intermediate

Output of a Model Versioning Command

What is the output of the following command when run in an MLflow tracking server with two registered model versions?

MLOps

mlflow models list-versions --model-name my_model

A Error: model name not found

B [{'version': '1', 'stage': 'Production'}, {'version': '2', 'stage': 'Staging'}]

C No models registered

D [{'version': '1', 'stage': 'Archived'}, {'version': '2', 'stage': 'Archived'}]

💡 Hint

Consider what MLflow shows when models have versions in different stages.

The command lists all versions of the model with their stages, showing production and staging versions.

🔀 Workflow

advanced

Identifying Technical Debt in an ML Deployment Workflow

Given this simplified ML deployment workflow, which step introduces the most technical debt?

Steps:

1. Data collection
2. Manual feature engineering without documentation
3. Model training with fixed hyperparameters
4. Deployment without automated monitoring

A Step 3: Model training with fixed hyperparameters

B Step 1: Data collection

C Step 4: Deployment without automated monitoring

D Step 2: Manual feature engineering without documentation

💡 Hint

Think about what makes future changes and debugging harder.

Manual feature engineering without documentation makes it hard to maintain or update features, increasing technical debt.

❓ Troubleshoot

advanced

Troubleshooting Model Drift Detection Failure

An ML system uses automated drift detection but fails to alert when the input data distribution changes. What is the most likely cause?

A Drift detection configured with incorrect feature references

B Model retraining frequency is too high

C Data pipeline is fully automated and tested

D Model performance metrics are logged correctly

💡 Hint

Consider what would prevent drift detection from noticing changes.

If drift detection monitors wrong features, it won't detect actual distribution changes, causing missed alerts.

✅ Best Practice

expert

Best Practice to Reduce Technical Debt in ML Systems

Which practice most effectively reduces technical debt in ML systems over time?

A Implementing continuous integration and continuous deployment (CI/CD) pipelines with automated testing for data and models

B Manually updating model code only when performance drops

C Using ad-hoc scripts for data preprocessing without version control

D Deploying models directly to production without staging environments

💡 Hint

Think about automation and testing to catch issues early.

CI/CD with automated tests ensures changes are validated and reduces hidden errors, lowering technical debt.

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