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.