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.