![]() From what I have seen, in a very mature company in ML (a FAANG let's say), where they spent decades investing billions in their data and data infrastructure, they can take the luxury to invest time in developing more advanced models. The only ways to improve is by using better data, better infrastructures, better models or better use cases. The iterative nature of Machine Learning calls for continuous cycles of improvements. I don't think there is a company on this earth with a perfect data infrastructure such that any Machine Learning person can just go and serve their models with just the data they happen to need. ![]() Don't get me wrong, there is no downside to learning but if you think about the skills you need to have to be successful as a Data Scientist or a Machine Learning Engineer, chances are that SQL will take priority over more Machine Learning knowledge. You should probably learn advanced SQL techniques before you learn advanced Deep Learning techniques.
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