Python is known to be one of the most popular languages out there for developers. One Python-based framework that has also received a lot of attention is Django. This framework is used for building web applications in a secure and quick manner. Other frameworks also exist for Python, such as Flask and Tornado. However, Django is often the most popular due to its ease of use and reliability when building web applications. There are several other reasons as to why Django is a good choice for your next project.
Due to its versatility, Django is often recommended as the go-to Python web framework. It is mostly used in back-end applications, but has uses for front-end as well. One interesting way you can use Django is by integrating Artificial Intelligence (AI) and machine learning into your web application.
One question you might be asking right now – how exactly does Django work? Let’s take a closer look:
What does the architecture of Django look like?
Additionally, Django follows the Model-View-Controller (MVC) pattern very closely, but is named differently. Instead, Django is known to be a Model-Template-View (MTV) framework. This means that the terms view and controller are replaced by the terms template and view respectively. It’s important that we understand each of the terms as well:
Model: Models are involved in the back-end development of the web application, along with the organisation of data in databases.
Template: Contrary to models, templates are involved in the front-end development of the web application. This is what the user can see and interact with.
View: Views are in between models and templates, meaning that it isn’t exactly back-end or front-end. They act as an intermediary between both and pulls the entire pattern. together. It also controls the way in which data is displayed. You can think of the controller as the brain of the pattern.
Now that we have a better idea of Django’s architecture, we can take a deeper dive into how AI and machine learning integration works with Django.
How can Django be used for AI integration?
One package that is essential for AI integration is the django-ai package. It is a collection of apps that can be used to be to integrate statistical models into the project. This is done by integrating various libraries and engines, giving more tools for freedom in any of your Django projects. It allows for 2 types of statistical models to be integrated: low level and high level.
Low level models are basic and are the foundation for applications such as machine learning. One such example includes Bayesian Networks, famously used in machine learning. Neural Networks are known to be used for deep learning, and are also an example of a low level model.
High level models are more complex – they build off the foundations of low level models.
AI can involve various complex algorithms that can be difficult to manage. However, the simplicity of Django means that this is less of an issue.
How can Django be used for machine learning integration?
Machine learning is a type of AI that uses data to predict outcomes. It has a multitude of uses, and has been used as a valuable asset by various industries. Businesses are able to use machine learning algorithms to analyse market trends and make accurate predictions in financial analysis. Machine learning has even seen usage in the healthcare industry, such as medical imaging and drug discovery. You can drastically reduce the amount of manual data entry you have to do by using Django. The list goes on – but let’s look at how Django can be applied to integrate machine learning.
The django-ai package can also be applied to integrate machine learning into the Python web application project. Specifically, it contains an application to integrate supervised learning. There are 3 types of machine learning: supervised, unsupervised and reinforcement learning. A brief explanation of each one:
Supervised learning: Commonly used to predict outcomes, supervised learning works by using labelled datasets. It is extremely useful for classification and regression problems. This is the type of learning that the django-ai application focuses on.
Unsupervised learning: Unsupervised learning works by using unlabelled datasets instead, allowing the algorithm to find hidden patterns without any additional input. It can carry out more complex tasks than supervised learning, but it is more volatile in the fact that it may not be accurate every time. Some applications you can use unsupervised learning include clustering and association tasks.
Reinforcement learning: This type of learning involves training the algorithm to learn via trial and error, along with the usage of previous experiences. A huge advantage of this type of learning is that it doesn’t need data collection beforehand. The model can immediately start without any human supervision, unlike supervised and unsupervised learning. A couple of applications include scheduling and calibration.
The django-ai package works by focusing on a particular supervised learning model: the Support Vector Machine (SVM). SVM is an algorithm mainly used for classification problems in machine learning, but also has a few uses for regression problems.
Another application that the django-ai package contains is the Bayesian network model, integrated via the BayesPy framework. Bayesian networks are a probabilistic graphical model made of nodes and directed edges. The structure is made up of a Directed Acyclic Graph (DAG), which represents causal relations between variables. One use for Bayesian networks is knowledge discovery. The django-ai package gives you access to various features that support Bayesian network modelling.
To sum everything up:
Django is a highly flexible and effective web framework that you can apply to a variety of projects. It mostly sees back-end applications, and follows the MVC pattern in its architecture. The ease of use and security of Django makes it a great choice, no matter what project you might be currently working on. Using Django for AI and machine learning integration is perhaps a more overlooked usage of the framework.
On the other hand, you’ll be able to carry out successful integration of AI and machine learning using the package django-ai. This package is designed specifically to make the AI and machine learning integration process easier for you. It contains applications such as Bayesian Networks and Supervised Learning. Integrating AI and machine learning in your project will allow you to take full advantage of your data by integrating statistical models. This process is made seamless and convenient by using the django-ai package. Additionally, there are other packages that can speed up Django development. Enjoy working on your Django projects!
Researched and written by Saleem Maroof.