Since that 2015 observation, ML models and frameworks have been built that make it relatively easy to avoid the most glaring potholes in the way of the ML practitioner.
most things sold as MLOps are overkill and unnecessary for most teams.
As long as your ML system is simple enough that an ops person can (1) find out when it isn’t working properly, (2) make small changes to it, and (3) redeploy the model, you are achieving the goal of MLOps.
You don’t need to build complex automated drift detection if someone can easily launch a retraining job if they see the model drifting.
Simpler solutions exist
The majority of problems “solved” by MLOps solutions are already well addressed by standard data processing approaches. You don’t need special ML processing architecture.