Is DevOps a necessity?
Under Engineering Practice, software engineers are empowered to have responsibility developing high quality products through use of test driven development and other engineering practices that enable them to build features in agile fashion.
Engineering teams are enabled to rapidly iterate through features. These features may be empirically tested with customers such that only customer impacting features are productized. Such a process will improve performance to a team to delivery features that impact business goals. Many organizations have been practicing the process to achieve their business goals.
Can we apply DevOps philosophy to ML Engineering?
The sort answer is yes; however, AI/ML teams faces unforeseen challenges that a standard software engineering team normally do not encounter. That is DATA. Data Science a.k.a AI/ML Engineering team cannot function without DATA. Their existence is based on DATA. Of course, software practice apply to them and they also use them. However, if the team does not have access to DATA, their work will be in vain.
An organization may have the best DevOps implemented with fully automated process to get into production quickly that build your model and deploy into production in no time; but if the team does not have data to build and test their AI/ML models, those model will be un-predictive and useless in production with no value to business.