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C Using outdated data schemas without updating the pipeline components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Using outdated data schemas causes components to break or behave unexpectedly, creating technical debt.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.