How to Serve and Deploy Machine Learning Models Easily

Moving from Jupyter notebooks to production is not that difficult after all

Photo by Fran Jacquier on Unsplash

If you’re a data scientist, you probably spend a lot of time developing intricate Jupyter notebooks to perform data analysis, build complex training pipelines, or compute statistics.

Jupyter notebooks are great for this and allow us to prototype ideas in no time.

But, what happens once you’re done with this work and you’re satisfied with your saved ML models? 🤔

This is where you start to think about deploying them to production. Have you thought this through when you started working?

Probably not. And you’re not to blame as this is not a data scientist’s core expertise. (although the industry is currently moving towards this)


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