Why Technical debt in ML systems in MLOps? - Purpose & Use Cases

The Big Idea

What hidden traps in your ML code could be silently slowing you down?

The Scenario

Imagine building a machine learning model by manually tweaking code, data, and settings every time you want to improve it or fix a bug.

You keep adding quick fixes without cleaning up old parts. Over time, the system becomes a tangled mess that's hard to understand or change.

The Problem

Manual updates take too long and often break other parts without warning.

It's easy to lose track of what changes were made and why, causing confusion and repeated mistakes.

This slows down progress and frustrates teams trying to deliver reliable ML solutions.

The Solution

Recognizing and managing technical debt in ML systems helps teams keep their code, data, and models clean and organized.

It encourages building with best practices, automating tests, and documenting changes so the system stays healthy and easy to improve.

Before vs After

✗ Before

def train():
    # quick fix for data
    data = load_data('old_path')
    model = train_model(data)
    save_model(model, 'model_v1')

✓ After

def train():
    data = load_data(config.data_path)
    model = train_model(data)
    save_model(model, config.model_path)
    log_metrics()
    run_tests()

What It Enables

It enables building ML systems that can grow and adapt smoothly without breaking, saving time and effort.

Real Life Example

A team deploying a fraud detection model can quickly update it with new data and rules without causing outages or errors, thanks to managing technical debt.

Key Takeaways

Manual ML updates create hidden problems that slow progress.

Technical debt management keeps ML systems clean and reliable.

Good practices help teams deliver better ML faster and safer.

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