MLOps vs DevOps: Why is MLOps different? (MLOps-2)

MLOPs makes ML development continuous in a never-ending loop

ML operations aim to accelerate, scale and sustain model development, deployment and maintenace.

MLOps is a typical Dev-Ops process, which is the sequence of actions taking an ML project from development to production, except MLOPs has a different nature than Dev-Ops due to the reasons listed below.

1. ML projects are experimental by nature, they require lots of trial and error in addition to testing, validation, integration and deployment before the the project is ready for production. Each of these steps can affect the data or the model in a way that ruins the predictions.
2. ML projects require frequent tuning and retraining due to the rapidly changing nature of data during the development and the runtime as well.
3. Many ML issues cannot be detected or realized until tested in the runtime, which makes the standard quality engineering process not sufficient to ensure the model is working. ML quality process requires continuous evaluation, continuous training and continuous deployment. This requires going through the whole process to deployment every time before we can ensure that the model is ready to be used.
4. ML projects have to be validated ethically before release to ensure that they do not reproduce discriminatory bias or repeat any unethical behaviours that…