Technical debt in ML systems in MLOps - Commands & Configuration

Introduction

Technical debt in ML systems happens when quick fixes or shortcuts in machine learning projects cause problems later. It makes the system harder to maintain, update, or trust over time.

When you want to avoid messy code that slows down adding new features to your ML model

When you need to keep your ML system reliable as data or requirements change

When you want to prevent hidden bugs caused by outdated or unclear model versions

When you want to make it easy for your team to understand and improve the ML pipeline

When you want to save time and money by reducing repeated work fixing avoidable issues

Commands

This command runs the MLflow project in the current folder, ensuring your ML code and environment are tracked and reproducible.

Terminal

mlflow run .

Expected Output

2024/06/01 12:00:00 INFO mlflow.projects: === Run (ID='1234567890abcdef') succeeded ===

Creates a new MLflow experiment to organize runs and track model training results clearly.

Terminal

mlflow experiments create --experiment-name 'model-training'

Expected Output

Created experiment with ID 1

→

--experiment-name - Sets the name of the experiment for easy identification

Runs the MLflow project with a parameter alpha set to 0.5, showing how to track different model settings to avoid confusion later.

Terminal

mlflow run . -P alpha=0.5

Expected Output

2024/06/01 12:05:00 INFO mlflow.projects: === Run (ID='abcdef1234567890') succeeded ===

→

-P - Passes parameters to the MLflow project run

Starts the MLflow user interface so you can visually compare runs, parameters, and metrics to spot technical debt early.

Terminal

mlflow ui

Expected Output

2024/06/01 12:10:00 INFO mlflow.ui: Starting MLflow UI at http://127.0.0.1:5000

Key Concept

If you remember nothing else from this pattern, remember: tracking code, data, and parameters clearly prevents technical debt in ML systems.

Common Mistakes

Not tracking model parameters and code versions

This causes confusion about which model produced which results, making debugging and improvements hard.

Always use tools like MLflow to log parameters, code versions, and data used for training.

Skipping experiment organization

Without organized experiments, runs get mixed up and it is difficult to compare or reproduce results.

Create and use named experiments to keep runs grouped and easy to find.

Ignoring the MLflow UI for monitoring

Missing visual insights means you might overlook model drift or errors that cause technical debt.

Regularly use MLflow UI to review runs and catch issues early.

Summary

Use MLflow commands to run and track ML projects with clear parameters and versions.

Create experiments to organize runs and avoid confusion.

Use the MLflow UI to monitor and compare model runs to prevent hidden 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