MLOps vs DevOps: What Is the Difference?¶

Introduction¶

DevOps and MLOps share automation, CI/CD, observability, infrastructure as code, and rollback discipline. MLOps adds one hard problem: behavior depends on data as much as code.

Why This Matters¶

An ML API can change behavior because training data changed, feature distribution shifted, or real-world behavior moved away from historical examples.

Core Concepts¶

DevOps versions code and infrastructure. MLOps also tracks data, features, experiments, model artifacts, evaluation metrics, and drift signals.

Practical Example¶

A pipeline may look familiar, but the training and evaluation steps are model-specific:

name: ml-api-ci
on: [push]
jobs:
  test-train-package:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - run: pip install -r requirements.txt
    - run: pytest
    - run: python pipelines/train.py --data data/sample.csv
    - run: docker build -t ml-api:${{ github.sha }} .

How This Fits in a Production Workflow¶

MLOps extends DevOps controls around the model artifact. A container image alone is not enough; you need to know which model is inside it and how it was trained.

Common Mistakes¶

• Assuming Kubernetes deployment solves model quality.
• Treating accuracy as a unit test.
• Ignoring data lineage because the service deploys cleanly.
• Monitoring HTTP 200 responses while predictions degrade.

Quick Checklist¶

• Track code, data, model, and image versions.
• Automate evaluation before deployment.
• Keep rollback paths for model and application.
• Monitor inference behavior after release.

Related Guides¶

• The MLOps Lifecycle Explained
• CI/CD Pipeline for Machine Learning Projects
• Model Deployment Strategies in MLOps

Summary¶

Compare DevOps and MLOps in practical engineering terms and learn why ML adds data, experiments, evaluation, and drift challenges.