Flask and Django are two of the most popular Python web frameworks available today, so it’s no surprise that they both have their own communities of machine learning experts who favor one framework over the other. With so much buzz around which framework is better, the question remains: Which one is easier to use? And which one is more suited to machine learning and data science projects? The answer might surprise you! Here, we compare Flask vs Django with respect to everything from their libraries to their deployment to their documentation to their API, speed, and performance.
Why use Flask or Django for machine learning?
Flask and Django are user-friendly development frameworks for Python that can be used to build a wide range of web applications, including machine-learning applications.
There are many options for web development frameworks when it comes to machine learning, but what makes Flask or Django great choices for this purpose?
First of all, both frameworks are easy to learn and use. Both allow you to quickly build an application with Python and then start integrating Machine Learning models into it.
In addition, there are several third-party libraries that make it easy to integrate Machine Learning models into web applications built with either Flask or Django.
Differences between Django vs Flask?
There are a few key differences between Flask and Django, which could affect your decision if you’re thinking of using either platform for your next project.
First of all, Django is a full-fledged web framework while Flask is just a microframework. This means Django offers more functionality than Flask does.
One of the areas where Django really shines is form handling. It gives you a wide range of form validation options that are not available in Flask
Database Management System (DBMS)
Another area where Django outperforms Flask is in object-relational mapping. In Django, you have full control over your database tables. This lets you easily build complex queries, modify existing tables and add new ones when necessary. On the other hand, you can only create database records using SQLAlchemy in Flask.
Security and Authentication
Another significant difference between the two frameworks is the authentication system. Flask uses Werkzeug internally to handle user authentication and sessions. In contrast, Django uses its own built-in authentication and session management components. The main advantage of this approach is that you don’t need external packages to get into work. In addition, it supports other features like password hashing and two-factor authentication. However, the fact is that it’s implemented using Python means that it’s slower than the Wekzeug approach.
Performance and Speed
When it comes to performance, Flask is actually faster than Django in most cases. This is mainly due to the fact that it doesn’t have all the features that Django has. So it often doesn’t need to perform as much work to render a page as Django does.
Finally, both frameworks have a large number of libraries that you can use to extend them. But there are some notable differences between the two. For example, Django has more packages for web development whereas Flask has many more for data analytics.
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Flask vs Django which is easier for data science
Flask and Django both are popular Python web frameworks for machine learning development. They both have their pros and cons, but which one is easier to learn and use for this purpose?
I found Flask relatively less complex to learn and implement. For Django, there are various modules like model, view, and template (MVT) you need to understand first before you start your development.
Within a day you can learn and develop a simple project using Flask. On the other hand, if you are thinking to work with Django, you need to spend a good amount of time clearing your basic knowledge.
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Both frameworks have their pros and cons you should select any one of them as per your requirement.
If you are working on a small machine learning project then I will recommend you to use Flask as it is easy to implement and deploy.
But if you are working on a complex web application development then forget everything and start with Django.
That’s it for today. If you have any questions or suggestions regarding this post please let me know in the comment section below.
Hi there, I’m Anindya Naskar, Data Science Engineer. I created this website to show you what I believe is the best possible way to get your start in the field of Data Science.