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Machine Learning for Frontend Development: The Road to Future

When creating a website, it is an essential objective of developers to make it appealing. They undergo a few systems, including drawing idea sketches, building models, and experimenting with the website before pushing it live.

These procedures don’t occur parenthetically. Developers spend many months on constructing a beautiful and responsive website. Be that as it may, the development in technologies is making things more manageable for them.

The cutting edge technologies like artificial intelligence (AI) and machine learning are quickening frontend development and making coding and testing of website format simpler, more agile, and frequently prolific.

What is Deep Learning?

Deep learning comprises two significant parts: training and inference.

Training includes implementing a large dataset to a new neural network and rendering the correct answer, i.e., a large number of inputs with the outputs attached.

The neural network takes all the information and can make an inference when bestowed with a new image based on the past dataset.

After the training, the neural network becomes competent to analyze and understand the input and then return the output. The deep learning models are primarily used for several use cases of AI, like computer vision and natural language processing.

Frontend developers can use related models for designing UI elements by training neural networks with associated data.

Training Neural Networks

There are two types of data that frontend developers can apply to train a neural network. The first one is a graphical user interface (GUI) screenshots, and the other one is whiteboard sketches. The screenshots and designs would hold their corresponding code.

This training will give the neural network ability to generate code from a GUI screenshot called pix2code. Whereas, the conversion of sketches to the system is called sketch2code. After investigating the screenshots and designs, the neural network will learn how to code these images in HTML or CSS. For models with text, the systems will further go through text recognition rounds.

Along with developing websites, equal or more time is spent on detecting and fixing the bugs. Automating the testing process with AI can produce a much-needed shift in the industry. AI can be applied to determine robust testing methods and quick bug fixes, thereby strengthening the criterion of development.

AI can even be used to detect viruses that no other compiler can detect. Microsoft research lab is operating on such a model that can identify the bug. The only thing you have to do is implement a small description of the problem, and the model will write a few lines of code and solve the said problem by itself.

Real-world examples

Enterprises of every size have begun to use this technology to power their UX design.

Airbnb, the largest community-driven hospitality company in the world, is employing AI to create its prototype. According to Benjamin Wilkins, the design technology leader at Airbnb, the time required to test an idea should be zero. With this goal, the company uses the prototype to create a functional design code from sketches.

Originally, Airbnb had trained the prototype with only a dozen hand-drawn sketches, machine learning algorithms, as well as some intermediary code for rendering elements from the design system into a browser.

On the other hand, Uizard, a startup in Copenhagen, uses the notion of transforming images into HTML codes for its entire business. The startup has developed a machine-learning algorithm that can read images and return the customizable and production-ready system linked to the platform.

Uizard is utilizing this concept to the development of mobile and web applications. Similarly, many companies are looking to hire frontend web developers to train them for emerging tech.


Shift is befalling, and technology is evolving at a high pace. As artificial intelligence is becoming more prevalent and universal, these technologies are assumed to find use in every phase of our life. There is no doubt that AI can make the lives of frontend developers simple by automating monotonous tasks.

Whether AI will conceive surpassing humans and start developing self-improving applications prevails as one of the biggest questions that need to be answered in the years to come. This article was to provide a brief idea about the impacts of AI on frontend development at present and in the coming years.

Also, web developers and companies will have the option to foresee the time required for the generation of a task. In the long run, the improvement of time, assets, and design spending strategies can encourage organizations to diminish expenses and increase the degree of profitability (ROI).

Inside the following couple of years, experts predict that AI and machine learning will change how developers take a shot at frontend development. It will expedite the prototyping and break the barriers of programming development.

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