With the rise of AI and the word “model” becoming almost mainstream, I figured it might be helpful for me to do a mini-series on some basis around MLOps (Machine Learning Operations).
Probably one of the biggest misconceptions I see when it comes to Machine Learning in general (as someone who has been working with it in production for over a decade) is that people think it’s all smoke, mirrors, and black magic.
The truth is very different.
Sure, I’ve seen plenty of Data Scientists and Machine Learning Engineers doing their black magic with model configs while pontificating on p-values. But that is the smallest portion, and arguably the easiest part of putting Machine Learning models into production.
The hard part is MLOps.
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