Data Scientists NEED to learn to package and deploy their own models.
I’m not being a gatekeeper here, I’m giving you facts. I interview, hire, and lead data professionals, whether it be Data Scientists, Data Analysts, Machine Learning Engineers, or Data Engineers. Packaging and deploying models are consistently gaps for people without a software engineering background.
That’s why in this article and video I’ll show you a rapid deployment of a Natural Language Processing (NLP) app, from start to finish. I’m not worrying about developing the model because modeling isn’t the gap I see in the market.
So, you’re building a python app using poetry and you want to use a Jupyter Notebook in the same environment. It can be a pain but don’t worry, I’ve got your back.
In this post, I’m going to show you how to add a Jupyter kernel to your poetry environment. That way, you can avoid all the version conflicts and import issues that slow your progress to a halt.
If you’re using poetry for package management, you need to run this in your terminal:
poetry run python -m ipykernel install --name kernel-name
So, let’s get into what each of these…