AI Job Training: Junior MLOps Engineer - Day 4: DevOps vs MLOps

 

Goal

Understand the similarities and differences between DevOps and MLOps, and why data and models fundamentally change how systems are built, deployed, and maintained.

⏱ 45 min learning + 15 min notes

Introduction

Modern software teams rely on DevOps to ship code faster and more reliably.
But when machine learning enters the picture, traditional DevOps practices are no longer enough.

Why?

Because models are not just code — they depend on:

• Data that constantly changes

• Training processes

• Statistical performance instead of binary pass/fail

This is where MLOps comes in.

Key idea:
DevOps keeps software running.
MLOps keeps intelligence working.

Core Concepts

1️⃣ What DevOps Focuses On

DevOps is about code-centric systems.

Main responsibilities:

• Source control (Git)

• CI/CD pipelines

• Infrastructure as Code

• Monitoring uptime & errors

Typical question DevOps asks:

“Did the code deploy successfully?”

What MLOps Adds

MLOps extends DevOps to data-centric systems.

Additional responsibilities:

• Data validation

• Model training pipelines

• Model versioning

• Performance monitoring (accuracy, drift)

• Retraining automation

Typical question MLOps asks:

“Is the model still correct?”

CI/CD vs CI/CT/CD

DevOps: CI/CD

Stage	Meaning
CI	Continuous Integration (code tests)
CD	Continuous Deployment (ship code)

MLOps: CI/CT/CD

Stage	Meaning
CI	Test data pipelines & code
CT	Continuous Training
CD	Deploy trained models

Models must be retrained, not just redeployed.

Why Data Changes Everything

Aspect	DevOps	MLOps
Primary asset	Code	Data + Model
Failure type	App crash	Silent wrong predictions
Testing	Deterministic	Probabilistic
Versioning	Code versions	Data + model versions
Monitoring	CPU, errors	Accuracy, drift, bias

A model can be “up” but completely wrong.
This is the biggest mindset shift.

Conclusion

DevOps helps software run.
MLOps helps AI stay right.

If DevOps is about speed and stability,
MLOps is about trust.

In the real world, models don’t fail loudly — they fail quietly.

Mastering MLOps means mastering the intersection of code, data, and automation.

Hands-On Exercise (15 min)

✍️ Exercise: Deploying an API vs Deploying a Model

Fill in this comparison table:

Step	API Deployment	Model Deployment
Input	HTTP request	Live data
Testing	Unit & integration tests	Data validation + metrics
Build artifact	Container	Model file + metadata
Deployment	Push code	Push model
Monitoring	Errors & latency	Accuracy & drift
Update trigger	Code change	Data change

👉 Reflection Questions:

1. Why can a model fail even if the code didn’t change?

2. What happens if retraining is skipped?

3. Who should own model performance — devs or data teams?

Next:

Junior MLOps Engineer - Day 5: Production ML Failures
https://www.wisemoneyai.com/2026/01/ai-job-training-junior-mlops-engineer_23.html

Related video/s:

Day 3 - Linux Shell for MLOps: https://www.wisemoneyai.com/2026/01/junior-mlops-engineer-day-3-linux-shell.html

Day 2 - ML Lifecycle Deep Dive: https://www.wisemoneyai.com/2026/01/junior-mlops-engineer-day-2-training-ml.html