Technical debt in ML systems in MLOps - Step-by-Step Execution

Process Flow - Technical debt in ML systems

Start ML Project

↓

Build ML Model

↓

Deploy Model

↓

Collect Feedback & Data

↓

Make Quick Fixes & Workarounds

↓

Accumulate Technical Debt

↓

System Becomes Hard to Maintain

↓

Refactor & Improve

↓

Repeat Cycle

This flow shows how building and deploying ML models quickly can lead to technical debt, making systems harder to maintain until refactoring happens.

Execution Sample

MLOps

def deploy_model(version):
    if version < 3:
        print("Using quick fix for deployment")
    else:
        print("Using stable deployment")

This code simulates deploying ML model versions, showing quick fixes for older versions that add technical debt.

Process Table

Step	version	Condition (version < 3)	Action	Output
1	1	True	Print quick fix	Using quick fix for deployment
2	2	True	Print quick fix	Using quick fix for deployment
3	3	False	Print stable deployment	Using stable deployment
4	4	False	Print stable deployment	Using stable deployment
5	End	-	-	Stop: version 4 reached, no more quick fixes

💡 Versions 1 and 2 use quick fixes adding technical debt; versions 3 and 4 use stable deployment, ending the quick fix cycle.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
version	1	2	3	4	-	-
Condition (version < 3)	True	True	False	False	-	-
Output	Using quick fix for deployment	Using quick fix for deployment	Using stable deployment	Using stable deployment	-	-

Key Moments - 3 Insights

Why do versions 1 and 2 print 'Using quick fix for deployment'?

Because their version number is less than 3, the condition (version < 3) is True, so the quick fix branch runs as shown in steps 1 and 2 of the execution table.

What happens when the version reaches 3 or higher?

The condition (version < 3) becomes False, so the stable deployment branch runs, avoiding quick fixes and reducing technical debt, as seen in steps 3 and 4.

Why is using quick fixes considered technical debt in ML systems?

Quick fixes solve problems fast but add complexity and hidden issues that accumulate over time, making the system harder to maintain, as represented by the flow from quick fixes to accumulated technical debt.

Visual Quiz - 3 Questions

Look at the execution table, what is the output at step 2 when version is 2?

A Error

B Using quick fix for deployment

C Using stable deployment

D No output

💡 Hint

Check the row where Step is 2 and version is 2 in the execution_table.

At which step does the condition (version < 3) become False?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the Condition column in the execution_table and find where it changes from True to False.

If we change the condition to (version < 5), how would the output change at step 4?

A Output would be 'Using quick fix for deployment'

B Output would be 'Using stable deployment'

C No output

D Error occurs

💡 Hint

Check the condition logic and output for version 4 in the execution_table.

Concept Snapshot

Technical debt in ML systems happens when quick fixes and shortcuts accumulate during model deployment and maintenance.
These shortcuts make the system harder to maintain over time.
Detect technical debt by tracking quick fixes in deployment code.
Refactoring and stable deployment reduce technical debt.
Always aim for clean, maintainable ML pipelines.

Full Transcript

Technical debt in ML systems occurs when teams apply quick fixes or shortcuts during model development and deployment to meet deadlines or handle unexpected issues. This leads to accumulated complexity that makes the system harder to maintain and evolve. The example code shows a deployment function that uses quick fixes for older model versions (less than 3) and stable deployment for newer versions. The execution table traces how versions 1 and 2 trigger quick fixes, adding technical debt, while versions 3 and 4 use stable deployment. Key moments clarify why quick fixes happen and their impact. The visual quiz tests understanding of condition changes and outputs. The concept snapshot summarizes how to recognize and reduce technical debt in ML systems by avoiding quick fixes and refactoring pipelines regularly.

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