How Django helped in rapid application development especially for Machine Learning projects

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How Django helped in rapid application development especially for Machine Learning projects:

 

Data scientists are quite comfortable in writing their algorithms or training their machine learning models in Jupyter notebooks. But, when the time comes to convert your Jupyter notebook code into a fully functional service which can handle production loads, we often find ourselves struggling to find out an optimized solution. The two most common frameworks which are used across the industry are Django and Flask.

 

Now, firstly talking about Flask, it provides an easy way to get a basic Python API up and running with very little configurations required. Also, Flask is lightweight and keeps your deployment sizes low. However, flask has its own drawbacks. The HTTP server included with Flask is not capable of handling production loads, hence you need to use clients like gunicorn along with Flask. Also, for proper request handling, you might also need nginx setup in front of gunicorn. So, for production level systems, the setup becomes complicated, and you do not get much capabilities out of the box in Flask. It is absolutely good for running small Proof-of-Concept models and APIs, or some application with less complicated and fewer API endpoints.

 

If we have to focus on full-stack development along with supporting our machine learning algorithms and models, we should definitely consider Django Framework. Django is a powerful Python framework which allows you to build full-stack applications with Database support and other integrations with a number of useful features available out of the box. Django allows you to handle all the API routings, Database migrations natively. You can also use plugins like Django Rest Framework, Kafka Logpipe, etc. for further enhancing it’s capabilities. You can also use gunicorn and nginx setup along with Django as well for an even better, resilient architecture. 

 

So, summarising our experience with both the frameworks, Flask is an extremely useful, lightweight option for small machine learning projects with not so complicated business logic requirements, whereas, Django is a powerful Python framework for full-stack application development, and is synonymous to Spring Boot in Java. Using other plugins to enhance functionality is easy in both the frameworks and you should select one based on your project scope and requirements.