Future Impact of machine learning on website development

Since the creation in 1989 of the World Wide Web called Web; this technology and its use continues to evolve and progress and this in a dazzling way, the World Wide Web passed from Web 1.0; which just happens to be an information portal where users only passively receive information without having the possibility of at least posting comments. Web 2.0 which offers more possibilities to the user, in particular in terms of sharing information such as Youtube, Wikipedia or Meta (Facebook); to soon arrive at Web 3.0 in which machines can process information and interpret information in an intelligent way and this to adapt to the needs and all the preferences of the users.

Again, when we talk about machine intelligence (MI) or the processing of information or adaptation to user preferences, we are obviously talking about artificial intelligence and data science; and processing an enormous volume of data to develop intelligent and powerful models capable of adapting to user preferences; an adaptation that inevitably requires the use of operational machine learning tools (MLOps) , tools that verta makes available.

What is the Smart Web?

By using the internet nowadays, we realize that the navigation in the different websites has become oriented, in the sense that the website offers us articles, videos or other web content which are very close to our preferences. We therefore feel that we are in front of a certain intelligence, this intelligence is what is called smart Web or Web 2.0 and Web 3.0 .

Smart Web or Semantic Web is a version of the Web that acts intelligently and analyzes user’s choices and preferences to provide optimal use of the Internet.

The smart Web is defined as a web woven on a set of data that is directly or indirectly processed by machines using MLOps , deep learning and artificial intelligence models in order to help the user create new knowledge.

This intelligent web uses algorithms based on mathematical and statistical models previously designed using databases collected from the web, databases containing golden information.

How does it work?

The first question everyone asks is: How can a machine, especially a computer through a web browser offer web content, guide the user through the net or even choose outright for him ? Well, the answer is simple: it is thanks to the use of data science; machine learning (ML), deep learning in the world of the web and the internet.

1) The data collection

The sources of the data used by machines are diverse, but they are mainly collected through the use of the famous cookies, these cookies are quite simply small files in text format which generally save under the authorization of the user of the web browser. , these cookies keep track of the searches carried out and are transmitted by internet to the owner of the web browser such as Google Chrome; the Web browser offers web content directly to the user, and uses all the cookies collected for more in-depth uses such as the overall orientation of internet research for a given geographic area or category.

2) Learning stage

Before being able to offer web content and practically interact with the user; the machine or browser must first go through the learning stage ; this learning simply consists of collecting data relating to the usual user of the machine or to the various users of the machine.

Thus, the browser offers internet content based on the history of keyword searches carried out by the user and gives possible answers. This is just by entering part of a key word on the browser, which is like magic! But in reality, this is just reading a cookie file containing the user’s search history.

This learning step, as explained, only concerns the direct search part on the net, there is obviously more sophisticated learning which requires the use of advanced intelligent models.

Another type of learning exists; it’s about Advanced learning.

Advanced machine learning is a complex combination of statistical data and statistical models, including predicting user behavior on the web.

The behaviors to be expected are multiple and above all very varied; they can be keywords that are frequently searched for by a user or users in the same geographical area or having the same characteristics such as age or sex, it can also be an online sales site that analyzes its database and pulls out the purchasing habits of a person or a group of people, smarter models outright foresee the opportunity to put on sale and given product and thereby optimizes the turnover on the sales site.

3) Tests et validation

Before being able to use a machine learning or deep learning model, it is very important to test the model with a large enough amount of data to be able to ensure its relevance and effectiveness, it is also necessary to test and compare different versions of the same machine learning model in order to be able to select the best model in terms of efficiency and optimality.

It is with this in mind that machine learning operational platforms (MLOps) exist, these platforms offer the possibility of training artificial intelligence models, they offer to store statistical models like a hub model and test them on a large volume of data in order to develop and concept the best possible model, this important step is part of what is called the life cycle of a machine learning model.

Other smart web applications

The intelligent web also concerns options offered directly by the web browser or web browser; options like speech recognition; this option can be extremely useful for the user, especially the disabled user; this is an option completely based on artificial intelligence and therefore data science.

It is also envisioned that websites offer the possibility of use to blind users; this use is characterized by a direct explanation of the content of the web to the user.

This work is done through the use of appropriate electronic tools, they can allow sharp command to the user, ultimately making them a normal user.


The development of the technology is ultimately dedicated to maximizing profits and large corporations, but they also aim to make life easier for the user and make the web more welcoming and overall, more accessible.

Such a performance of technology passes through the use of data science through platforms offering operational machine learning tools (MLOps).