Mlops is the process of combining the end-to-end process of design, development, test, and delivery of machine learning models in a single step. Mlops is just inheriting the good parts of devops in to the ml space.in software space only the code matters whereas in ML both the code and data matter.
Benefits of Mlops:
When there are many different teams working in an organization like the data engineering team, ML Dev team, and operations team there is a bottleneck when the work of one team doesn't work well with the other team's code.
This can be done by using version control and the next version is approved only when the end-to-end pipeline is tested to work.
With the pipeline approach, the updates performed by each of the teams can be cross-checked before pushing their changes.
Machine learning comes under tight scrutiny for explainability because humans do not explicitly specify any learning logic into the models thus it is essential to specify how the models were trained and how the data was collected.
Mlops also helps with domain shift problems as models can be trained, data collection all can be done on the fly.