Deploy Your Model with Python Streamlit
Once your machine learning model is built, you are ready to deploy it for real use. Do you know there are requirements that you should have considered early in data pre-processing and modeling? I call them modeling through the lens of model deployment. The failure to incorporate those requirements may result in irreversible errors. The purpose of this article is twofold: It will show you how to incorporate those requirements to support an “error-free” prediction app; it also will show you how easy it is to deploy your model with Python Streamlit. So read on! You will be glad that you have read this!
(A) Data Science Modeling Cycle
Figure (I) shows the entire data science modeling process: from client engagement to data preparation, from prototyping a model to advanced modeling techniques, and from model deployment to performance monitoring. Click “Following the Data Science Process”.
Although this process seems one-way, there are requirements that you should have considered before deploying your model.
