The first one I think is that more and more people are now using/trying to use machine learning models in production and they discover that the workflows and tools they used to use and work with are not suited for delivering machine learning models in fast, repeatable, and simple way.
The second reason is that I objectively think that a machine learning pipeline or CI/CD system is a bit different than the one used for pure software engineering practices, partly because machine learning does not only involve code, but more layers of complexity: data, artifacts, configuration, resources... All these layers can impact the reproducibility of a "successful build". Hence, a lot of engineering is required to both ensure that teams can achieve both reproducible and reliable results, and increase their productivity.