Difference Between DevOps, DataOps & MLOps: MLOps and Production AI Guide (2026)

Difference Between DevOps, DataOps & MLOps in MLOps and Production AI

Beginner Topic 3 of 9

Why This Comparison Matters

Modern AI systems require collaboration between multiple disciplines. DevOps, DataOps, and MLOps serve different but connected purposes.

DevOps

Focuses on software delivery automation, CI/CD, infrastructure management.

DataOps

Manages data pipelines, ETL workflows, and data quality governance.

MLOps

Extends DevOps principles to ML models, ensuring reproducibility, deployment automation, and monitoring.

Key Differences

DevOps manages applications
DataOps manages data pipelines
MLOps manages machine learning models

All three must work together for production AI success.

MLOps Lifecycle Explained: From Data to Production CI/CD Pipelines for Machine Learning Models

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