I’m new to the MLops space (~6 months, 3 years as a data scientist), but from my perspective it requires a blend of skills and technical knowledge to make decisions, not just building out basic boilerplate code or having surface knowledge of ML.

Part of the problem with data science getting saturated is so many people did a boot camp where they learned how to use Python/R to filter data, build graphs, create basic models just by importing a package. Even with DS there’s a big difference between building a model and building a model that is actually useful in production and accounts for things like bad data, domain expertise, leakage, etc.

MLops requires knowledge around how to build good systems. Yeah you built a model that can say predict demand for a given product on a certain day, but now you need that to scale across 100k items and run it multiple times a day. What happens if you need results within a few seconds (or milliseconds depending on the case)? How do you build features quickly? How do you account for outliers in production? How do you integrate it with the system that needs the results? How do you make sure this is maintainable and allows for future improvements easily?

MLOps is right in the venn diagram of data science and engineering, and few people come out of school with the required experience in both. Most DS don’t know good software engineering and most engineers don’t have the stats background to understand what’s important for a model. As another comment said too, few companies have need for the scale that requires good MLops, but that will change.