Technical debt in ML systems in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is technical debt in ML systems?

Technical debt in ML systems means shortcuts or quick fixes in machine learning projects that cause problems later, making the system harder to maintain or improve.

beginner

Name one common cause of technical debt in ML systems.

One common cause is poor data management, like using inconsistent or unclean data, which leads to unreliable models and harder fixes later.

intermediate

How does lack of automation contribute to technical debt in ML systems?

Without automation, repetitive tasks like training or testing models are done manually, increasing errors and slowing down updates, which builds up technical debt.

intermediate

Why is version control important to reduce technical debt in ML systems?

Version control tracks changes in code and data, helping teams avoid confusion and mistakes, making it easier to fix issues and improve models over time.

advanced

Explain the impact of hidden feedback loops as technical debt in ML systems.

Hidden feedback loops happen when model predictions influence future data, causing bias or errors that are hard to detect and fix, increasing technical debt.

What does technical debt in ML systems mainly cause?

A More work and problems later

B Faster model training

C Better data quality

D Automatic error fixing

Technical debt means shortcuts that cause more work and problems later.

Which practice helps reduce technical debt in ML systems?

A Ignoring data quality

B Manual repetitive tasks

C Using version control

D Skipping testing

Version control helps track changes and reduce mistakes, lowering technical debt.

What is a risk of not automating ML workflows?

A Slower updates and more mistakes

B Easier collaboration

C Better model accuracy

D Fewer errors

Without automation, updates are slower and errors increase, adding technical debt.

Hidden feedback loops in ML systems can cause:

A Improved model fairness

B Bias and hard-to-find errors

C Faster data processing

D Automatic data cleaning

Hidden feedback loops cause bias and errors that are difficult to detect.

Which is NOT a cause of technical debt in ML systems?

A Poor data management

B Skipping testing

C Lack of automation

D Good documentation

Good documentation helps reduce technical debt, not cause it.

Describe what technical debt means in machine learning systems and why it matters.

Think about shortcuts and their long-term effects.

List common causes of technical debt in ML systems and suggest ways to reduce it.

Consider both problems and fixes.

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