During the last decade we created a lot of content explaining basics of ML and raised a generation of people, who would like to develop/improve/apply ML algorithms. They plan to spend their time on parameter tuning and squeezing yet another % of accuracy. Somehow many people are convinced that life is some kind of Kaggle competition and the more accurate models you create, the better you will be paid.
To be honest, it was never the case.
However in the past you could find a "Data Scientist" job, where squeezing accuracy was your main responsibility. Many companies learnt in a hard way that very deep knowledge of ML algorithms is not enough to create value out of them. Therefore typical job description of "Data Scientists" role in 2024 contains phrase like this:
𝘚𝘵𝘳𝘰𝘯𝘨 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘰𝘧 𝘔𝘓 𝘢𝘯𝘥 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘪𝘯 𝘣𝘳𝘪𝘯𝘨𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘵𝘰 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯
Mysterious "bringing models to production" essentially means that you should know Machine Learning Engineering (MLOps, Cloud, CI/CD, etc). All Data Science jobs become either Machine Learning Engineering, either Data Analytics + Data Engineering. Pure Kaggle-like Data Science roles do not exist any more (or to be more precise exist only in research in very limited amounts).
There are 2 take-aways here:
1. If you're a Data Scientist and do not want to touch Engineering at all, it will significantly limit your career opportunities (Nowadays MLOps questions are asked even in interviews for management roles).
2. Many companies understand the need of MLE and are looking for people, but there is still a significant deficit of such specialists on the market.
However, learning MLE on your own might be challenging: to learn how to "bring models to production" you need to talk with someone who did it, just online course is not enough.
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