Flask vs. Django: Which Machine Learning Framework is Better?

• By Paige Griffin
• 23-03-2022
• Web Development

Python is insanely popular among machine learning enthusiasts these days. Hence, there is no denying that you will use it to develop a machine learning model.

The real challenge arises at the deployment stage because you can use many frameworks for the purpose. This may add to a lot of confusion. Hence, asking yourself which one to choose is completely valid.

In this blog, we will discuss two popular machine learning frameworks called Flask and Django. We'll also compare them side by side so that you can make the right choice. If you are also stuck in the deployment stage, hop in because this blog is for you.

A Basic Overview of Flask & Django

Flask

- A micro web framework that is written in Python.
- Easy to learn with straightforward implementation.
- Lesser Lines of Code.
- Primarily used by top companies like Netflix, Reddit, Mozilla.

Django

- A web framework built on Python.
- Open-source and accessible. It follows the MVC pattern(Model View Controller) which often confuses many beginners.
- Lines of code are larger than Flask.
- Companies like Pinterest & Instagram use this framework

A Few Criteria to Decide Between Flask & Django

Deciding on which python framework to choose among Flask vs. Django depends on many factors. Let's compare them one by one:

1. Your Familiarity with Python

Flask is suited if you are a complete beginner or intermediate in Python. The easy structure of the framework will ensure you can deploy your machine learning model without any hassle. However, if you are at the advanced stage of Python, Flask will seem rudimentary, Django will suit you more. Its native development features can save you a lot of time.

2. Size of Your Machine Learning Project

Flask is more suitable for simply trained machine learning models as compared to Django. Here are a few reasons why:

- The fully-featured Django framework can be a bit too much.
- Flask results in a less learning curve. Therefore, they have to spend less time getting acquainted with it.
- Less lines of code are written in Flask as Django relies on dependencies and specific folder structures. Flask also results in a cleaner code.

In simple words, flask is sufficient for most machine learning projects except a few complex ones. Although, if you are an advanced Python user, Django can offer a greater advantage.

3. The Database Management System You Want to Use

The choice of a database management system also plays a crucial role in deciding which Python framework you should use. Not that you can't use all the database management systems with Django and Flask. However, using non-relational data management systems with Django in your machine learning models can be complicated.

It is because Django relies on the ORM Django layer for creating "read, write, query, and delete" operations using the Python code in a relational database. However, it can't do the same for non-relational databases. This means if you are using the Django framework with a non-relational database, you will have to build a backend to support the same.

On the other hand, Flask doesn't have a native ORM. Therefore, how the framework interacts with databases depends on the ORM extension you would choose. Hence, Flask will be a better choice for non-relational database management systems.

4. Your Need for Authorization & Authentication

Both Django and Flask support authentication and authorization. The only difference is that Django comes with a pre-integrated package for this functionality. Using this package, you can configure users, groups, password hashing systems, etc.

On the other hand, Flask doesn't have built-in authentication and authorization functionalities. You will need extensions like Flask-login, Flask Mail, or Flask-WTF for this purpose.

So, if you want to save yourself from the headache of having to install different extensions, you can choose Django. Otherwise, Flask is good for you.

5. Handling of Forms

Django comes with a native form handling feature called ModelForm that allows both client-side and server-side validations and secures your machine learning model against threats like SQL injection, cross-site scripting, and cross-site request forgery.

However, Flask doesn't have any such feature. Instead, it relies on the Flask-WTF extension for creating an integration with WTForms.

6. Speed Benchmarks

Following are the benchmarks using which we can compare the speed of Django and Flask:

JSON Test

On average, Django returns a response in 42.52 milliseconds. On the other hand, 43.33 milliseconds is the average response for Flask. This means Django can handle 4,762 requests per second, while Flask can handle 4,630 requests per second.

Remote Test

On average, Flask returns a response in 3344.27 milliseconds from a remote server, while Django takes an average time of 3477.36. It means they both can handle 18.15 and 18.1 requests per second.

Complete Test

On average, Flask takes data load to render time of 1440.24 milliseconds. On the other hand, Django logs 2904.04 milliseconds - almost twice the time Flask takes.

So, Django may lag behind Flask in the complete test. Still, there are other speed benchmarks where it is comparable.

7. Community Support

Django has a larger community as it's been around for 6 years more than Flask. Despite this, both frameworks offer active community support. In simple words, you won't have to sacrifice community support by choosing either.

In Simple Words

Go with Flask if you are deploying a small machine learning model with a light codebase. But if you're planning to build something spectacular like Facebook, Django will be more suitable.

Another advice will be that if you are a beginner, go with Flask. Django is good, but you have to beat a longer learning curve.

So, I hope now you are clear on which Python framework you should choose for your machine learning model. If you still need guidance. Feel free to drop a comment. I will be happy to help.