Employing Python to make machine learning predictions can be a daunting task, especially if your goal is to create a real-time solution. However, Tensorflow and Scikit-Learn can significantly speed up implementation.
In this article, Toptal Python Developer Guillaume Ferry outlines a simple architecture that should help you progress from a basic proof of concept to a minimal viable product without much hassle.
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I love and use Django in lots of my personal and client projects, mostly for those involving relational databases and more classical web applications. However, by design, Django is very tightly coupled with its ORM, Template Engine System, and Settings object. Plus, it’s not a new project: it carries a lot of baggage from the past to remain backwards compatible.
In a few of my client projects, we’ve chosen to give up on Django and use a micro framework like Flask, typically when the client wants to do some interesting stuff with the framework. At the same time, we often need user registration, login, and more, all of which is easily handled with Django.
The question emerged: is Django an all-or-nothing deal? Should we drop it completely from the project, or is there a way to combine some it with the flexibility of other frameworks?
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