MLOps vs. DevOps: How are they different?
Search Enterprise AI, Friday, April 4th, 2025
DevOps and MLOps share common goals -- but managing machine learning models brings a whole new set of challenges.
DevOps is an approach to designing, developing and deploying software applications generally, while MLOps focuses specifically on machine learning models.
That, in a nutshell, is the main difference between MLOps and DevOps. But there's more to the story, especially because MLOps introduces novel requirements -- such as managing data versioning, handling model drift and retraining models -- that don't apply to most other DevOps contexts.
To fully understand how MLOps and DevOps compare, let's define each term and explore their similarities and fundamental differences.