MLOps emphasizes on data management and model versioning. DevOps prioritizes overall application performance and reliability. It prioritizes model performance in production and monitoring. It involves tasks such as testing and deployment automation
https://www.browserstack.com/guide/mlops-vs-devops#:~:text=MLOps%20emphasizes%20on%20data%20management,as%20testing%20and%20deployment%20automation.

From my understanding, Think of DevOps as the go-to team for building and shipping any kind of software smoothly. But when you're dealing with machine learning stuff, like training models and deploying them, you'll need MLOps. They're experts in handling the unique challenges of ML projects.

Is this right?