Pull your ML model out of your Server: The Database Solution

In the previous article, we saw one excellent reason you'd want to use tools like Streamlit and Gradio to deploy fast and deploy many versions of your Machine Learning (ML) application. We saw the advantages of the model-in-server architecture and why you'd definitely want to go down this road when you're prototyping. This is the easiest way to get quick feedback from a private circle of trusted testers and evaluate the market viability of your product idea. However, we concluded that when it's time to move into production, you need to rethink your design and pull your ML model out of your application server. Several issues, like programming languages, diverse scaling needs, and separate update cycles, make the model-in-server architecture approach a bad idea for production.