| DevOps | Combines development and IT operation processes to make things more efficient, reliable, and secure. |
| MLOps | A data science process that involves rapid testing and deployment of machine learning models used by data scientists & MLOps engineers. |
| DataOps | Focuses on data pipelines, providing valuable insights by connecting disparate data sources and having flexible data workflows at scale. |
| AIOps | Takes place within an organization’s IT operations department in AIOps rather than the machine learning team. |
| ModelOps | is enterprise governance and operations for models in production used by the IT or Business Operations team. |
| NoOps | aims to automate IT infrastructure in a way that negates the need for an in-house team for operational purposes. In this approach, all maintenance and similar tasks of an operations team are fully automated to such a level that means no manual intervention in the process is required. |
| DevSecOps | takes the traditional DevOps approach and adds additional security checks, code verifications, and deep testing into the workflow. Rather than having security be an issue at the end of the cycle, DevSecOps integrates security from the beginning of the workflow. |
| GitOps | focuses on using Git as a way to automate the rest of the continuous delivery pipeline. With Git as the single source of truth. |
| ITOps | focuses on stability and long-term reliability instead of advocating agility and aiming for speed. (Oposite: CloudOps) |
| CloudOps | focuses on distribution, stateless, and scalability. (Oposite: ITOps) |
| CIOps | The CI system is designed to run build and tests then deploy at varying levels of sophistication according to the complexity of the pipeline. Requires CI operators or administrators to configure the IT infrastructure required to support the new codes before deployment continues |