> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-engineering/mlops-teams.md).

# MLOps Teams

Someone has to own production ML. The question here is when that work sits with machine learning engineers and when it splits into a separate MLOps role, first as a staged structure and then as the impact one ML engineer has on the organization.

The same notes are in [Building Data/DS teams](/business-problems/data-science.md#building-datads-teams) and [Building Teams](/ai-product/management.md#building-teams).

The split does not happen on day one; it follows the team's stages. [Organizational structure for MLOps](https://www.newsletter.swirlai.com/p/sai-28-organisational-structure-for) by Aurimas Griciūnas: stages in a data science team's production development, when you need machine learning engineers, and when you need to split that into MLOps engineers and machine learning engineers.

Once the machine learning engineer is on the team, the next question is what that one role changes around it. [The Organizational Impact Of An ML Engineer](https://pub.towardsai.net/the-organizational-impact-of-an-ml-engineer-8facbfa42cd8) (March 2023) is the team note for this page.


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