Difference Between DevOps, DataOps & MLOps
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.
