# How does Great Expectations fit into ML Ops? | Great Expectations

**URL:** <https://discourse.greatexpectations.io/t/how-does-great-expectations-fit-into-ml-ops-great-expectations/413>\
**Category:** Archive\
**Created:** [September 27, 2020, 6:10am UTC](https://discourse.greatexpectations.io/t/how-does-great-expectations-fit-into-ml-ops-great-expectations/413 "2020-09-27T06:10:27Z")\
**Posts on this page:** 1\
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**Author:** ![kyle](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.greatexpectations.io/kyle/32/18_2.png) [@kyle](https://discourse.greatexpectations.io/u/kyle)\
**Post date:** [September 27, 2020, 6:10am UTC](https://discourse.greatexpectations.io/t/how-does-great-expectations-fit-into-ml-ops-great-expectations/413/1 "2020-09-27T06:10:27Z")

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[In our previous post on the importance of data quality in ML Ops](https://greatexpectations.io/blog/ml-ops-data-quality), we discussed how data testing and data validation fits into ML workflows, and why we consider them to be absolutely crucial components of ML Ops. In this post, we’ll go a little deeper into Great Expectations as one such framework for data testing and documentation, and outline some example use cases of deployment patterns and expectation types that suit the needs of an ML pipeline.

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This is a companion discussion topic for the original entry at [https://greatexpectations.io/blog/ml-ops-great-expectations/](https://greatexpectations.io/blog/ml-ops-great-expectations/)