Machine Learning Operations (MLOps) solves problems, unique to every aspect of the machine learning model lifecycle by melding tested DevOps approaches with data management best practices into a repeatable framework for model development, testing, and deployment. So that you could deliver new models just like any other type of application.
You already know how the standard ML project goes. Pipelines get hacked together with some brittle scripts. Continuous testing is a foreign concept. Collaboration goes messy with data and code snippets being lost due to poor model control. And that's just the development part.
Without any health monitoring, the fearless surviving models still burn in production. And your team needs to get back to the dreaded first step of preparing production ready-data and stitching together yet another bevy of tools that may (or may not) help to get that thing finally running.
Then, in the other room, sits the happy software development team. Their development is fast and flawless thanks to CI/CD with automation testing deployed at crucial checkpoints. Apps fit in their containers just fine and fly through the deployment pipeline without a hitch.