>Soon investors and CEOs will realize that ML is effective in narrow ways and that not everything needs ML. Sure but ML is also not being used in 90+% of the n

>Soon investors and CEOs will realize that ML is effective in narrow ways and that not everything needs ML. Sure but ML is also not being used in 90+% of the narrow use cases it is good for. >They will also realize that 1 ML team + ML as a service (Azure ML, Sagemaker, Google AI platform) is cheaper and works more reliably. These services replace part of the ML Ops component but not much else except in very narrow use cases. There's also already GUI based tools for building models but they're also not used much. I don't see this part of ML Ops as being the majority of what ML Engineers do so the majority of ML Engineers don't have to worry.