The simple difference
DevOps focuses on shipping software reliably. MLOps focuses on shipping machine learning systems reliably.
The difference is that ML systems depend not only on code, but also on data, models, features, experiments, and performance over time.
What DevOps handles
DevOps provides the foundation for automation and reliability.
- CI/CD pipelines
- Infrastructure as Code
- Cloud deployment
- Monitoring and alerting
- Security automation
- Release management
What MLOps adds
MLOps adds model and data lifecycle controls on top of DevOps practices.
- Model versioning
- Dataset tracking
- Experiment management
- Model validation
- Drift monitoring
- Retraining workflows
- Inference monitoring
Why AI teams need both
A model cannot provide business value until it is deployed, monitored, secured, and maintained. That requires both DevOps and MLOps discipline.
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FAQ
Is MLOps the same as DevOps?
No. MLOps includes DevOps principles but also handles model lifecycle, data versioning, model monitoring, drift, and retraining.
Do AI teams need DevOps?
Yes. AI teams still need CI/CD, infrastructure automation, security, and observability.