I have to frequently work with Data Scientists to learn their ML models and scale them in our production environments. A lot of times the models are not even st

I have to frequently work with Data Scientists to learn their ML models and scale them in our production environments. A lot of times the models are not even statistics, it's just linear algebra. They have a tendency to go for approximate algorithms when an exact algorithm could be written. I largely blame the company for this and not the data scientists. Every company wants to be in the ML game but may not have the volume and variety of data to warrant a use case. When your favorite hammer is ML then every data problem is a nail.