A step-by-step overview

Jupyter notebooks are where machine learning models go to die.

Wait— what?

Unlike what you probably learned in University, building models in a Jupyter notebook or R Studio script is just the very beginning of the process. If your process ends with a model sitting in a notebook, those models almost certainly didn’t create value for your company (some exceptions might be it was only for analytics or you work at Netflix).

That doesn’t mean that your models aren’t excellent. I’m sure they are. But it probably does mean the people paying you are not super excited by the outcome.

In general, companies don’t care about state-of-the-art models, they care about machine learning models that actually create value for their customers.

That process of going from a great model in a notebook to a model that can be integrated into part of a product or accessed by non-technical users is what I want to talk about in this post.

So, if you want your models to stop dying in notebooks and actually create value, read on.

#ai & machine learning #artificial intelligence #machine learning #model deployment #programming