It’s no secret that the deployment of the Machine Learning models conceptually is far from training those models and requires a different mindset. Some deal with it by having people dedicated to work on deployment since there’s actually a lot to do even when the model is still in the development phase and some just expect data scientists to do everything from modeling and analysis to deployment and monitoring.
In this talk I’d like to share my experience with deployment starting from 2017 as well as the lessons I’ve learned. Wait for a couple of wild and sometimes embarrassing stories but at least (oops) I didn’t do it again.
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