Machine Learning in Web Development

Usage of machine learning in web development. Best ML frameworks for web development.

The technology market is extremely oversaturated and businesses are looking to gain an edge and stay competitive by applying the latest trends in the web development industry. And machine learning is definitely the most popular and progressive solution in today’s web market, which has a significant impact on revolutionizing the entire industry.

Web development is permanently evolving and changing — outdated and standard solutions and concepts are being replaced by new strategies and innovative approaches. Thus, developers are constantly influenced by progress and are looking for the latest web trends that transform and improve the market. And possibilities of ML play a crucial role in this.

Today we are talking about the impact machine learning technologies are having on web development and product creation in 2022. Keep reading to learn about the significance of machine learning in web development, the key benefits of adopting this practice, and top ML frameworks. In this article, we rely on our own expertise and the best global industry experience.

The Importance and Role of ML in Web Development

In general, machine learning is the practice and science of making AI learn and act like a human, and making it continuously improve its learning and abilities based on real-world data provided. The purpose of this is a more accurate and improved performance of assigned tasks and functions. ML makes computers work without being explicitly programmed, and its algorithms figure out how to perform important tasks on their own by analyzing data and summarizing the examples they have.

In terms of web development, the practice of machine learning is an advanced technology that allows web products to learn by observing user behavior and preferences.

In the information world of the twenty-first century, people use billions of web applications that provide a variety of functions, ranging from simple mathematical operations to Internet banking systems that use government databases in their work and electronic document management. And the use of Machine Learning, in the future, can solve two problems at once — the automation of processes that previously required human participation and the rapid processing of large amounts of information with subsequent analysis.

What are the Benefits of Machine Learning in the Web Development Process?

Let’s find out what are the key advantages and main reasons for implementing the practices of machine learning and artificial intelligence into a web development project.

Enhanced Help Desk Options

The capabilities of artificial intelligence allow you to equip the site with chatbots, which definitely improve the work of the support service, and also allow you to serve users many times more productively. Such chatbots understand the natural language of people, distinguish emotions, and most importantly, they can answer any questions, even those that are not directly related to your product, service, or line of business. So, artificial intelligence takes a web project to a new level, and a chatbot allows you to improve customer service, which is actually the main goal of any business.

Improved User Experience

Artificial intelligence and machine learning technologies play an important role in determining user preferences. Artificial intelligence within the web project allows you to explore behavior patterns, search history, and geolocation. And this leads to the ability to customize the site and edit the interface so that it fully meets the needs of consumers.

Thus, adding these practices to web development will make the project as personalized as possible and users will perceive the site as if it was created specifically for them. Artificial intelligence and machine learning mimic real life in such a way that encourages users to stay on the site and make a purchase decision.

Accelerated Data Access

One of the latest trends that continues to gain momentum is voice search. And such functionality on the website, although not mandatory, is highly desirable. With the help of voice assistants, users can quickly and conveniently find the information they need, which greatly simplifies the search process.

Optimized Marketing

In terms of a marketing strategy, the technological capabilities of artificial intelligence and machine learning are used to make critical decisions, as well as determine demand-based forecasts.

These practices study user behavior patterns, determining what they like, what they need, and what their preferences are. This information directly affects the boost in sales and growth in conversion rates.

Thus, as we can see, the introduction of machine learning algorithms into a web project is definitely a profitable and beneficial solution. And this task is quite achievable.

Best ML Frameworks for Web Development

Obviously, in a software industry without an interface, libraries, and organized tools, web development is a nightmare. When these fundamentals come together, it becomes a framework or platform for simple, fast, and meaningful web development. Machine learning frameworks help developers define ML models in precise, transparent, and concise ways. Machine learning frameworks are used to provide out-of-the-box and optimized components to assist in model building and performing other tasks.

So, a framework is a set of tools and standard implementations to ensure faster development of any software product. Machine learning frameworks are constantly becoming more accessible to web developers due to open source machine learning tools that make it easy to create products.

Let’s take a look at the main ML frameworks to consider if you are looking to grow your business with machine learning techniques and practices. Remember that the success and efficiency of implementing machine learning algorithms into the web development process largely depend on the chosen toolkit.

Here are our top innovative frameworks that definitely deserve attention in 2022.


TensorFlow is a comprehensive open source machine learning platform. This data-driven programming solution is great for a wide range of tasks. It supports regressions, classifications, and neural networks, as well as sophisticated tasks and algorithms. It can be run on CPUs and GPUs. TensorFlow has complex features and paradigms, so it requires extra effort to learn.

It is a flexible structure and offers multiple models as well as versions that are serviced at the same time. Thus, it helps in non-automatic migration to new versions. The platform can run on GPUs, CPUs, servers, desktops, and mobile devices, so it’s portable. This is useful in R&D and provides automatic differentiation and performance.

TensorFlow brings machine learning technologies to the front-end and creates real-time interaction for users right in the browser. This framework is used to transform the web development process so that end users can get the most out of machine learning.


Caffe 2 is a deep learning instrument and a highly efficient machine learning framework written in C++. It supports basic circuits and is related to the development of neural networks. It provides both GPU and standard CPU-based acceleration, which is why Caffe 2 is popular in research projects and experimental development.

Caffe 2 is the fastest environment for deep neural networks and it supports GPU training for sample data. It has a well-structured MatLab and Python interface to work with.

Caffe 2 offers excellent performance and makes it ideal for industrial deployments as well as scientific experiments. It can process 60 million images per day with a single GPU and provides the fastest convention implementation.


Torch is a machine learning library and scientific computing environment. It comes with a scripting language interface from the Lua programming user interface. The Torch tool is used by Facebook, IBM, Yandex, and the Idiap Research Institute.

It is flexible and also offers high efficiency, speed, and many pre-trained modules. The most distinctive thing about Torch is numbers, not variations on number formats like integers, shorts, or doubles. Thus, it facilitates various operations and functions.

Apache Spark

Spark is a cluster computing framework written primarily in Java, Scala, R, and Python. It has Spark RDD data structures so there is no need to worry about Numpy arrays. It also allows working with Spark SQL frames.

Spark provides simple APIs for scientists familiar with R and Python. It is scalable and capable of running machine learning code on small and large computers. It is an upgraded end-to-end and compatible tool.


Scikit-Learn is a powerful Python library for ML and one of the most widely used Python packages for Data Science and Machine Learning. It is built on other libraries such as SciPy, Numpy, and matplotlib. This highly effective tool allows performing many operations and provides many algorithms. Scikit-learn also offers excellent documentation about its classes, methods, and functions, as well as a description of the algorithms used.

Scikit-Learn supports data preprocessing, dimensionality reduction, model selection, regressions, classifications, and cluster analysis. It also provides several datasets that you can use to test your models.

However, Scikit-learn does not implement everything related to machine learning. For example, it does not have comprehensive support for neural networks, self-organizing maps, association rule learning, and reinforcement learning. Scikit-learn is very efficient for data mining and supports various practical tasks. It is able to solve complex problems quickly and easily.

Microsoft CNTK (Cognitive Toolkit)

Microsoft created this framework with the intention of providing a deep learning toolkit and using it as a learning algorithm to learn machines from the way humans’ biological brains work. However, we can use CNTK to create various machine learning models such as Feedforward Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks.

This makes it easier for neural networks to navigate through huge and unstructured datasets. The tool offers fast learning times and intuitive architecture. You can customize it to suit your needs because it allows choosing your metrics, networks, and algorithms.

The Microsoft CNTK framework supports the ONNX format, which significantly affects the web development process using artificial intelligence and machine learning. It uses AI models with many frameworks, tools, runtimes, and compilers. CNTK is one of the best frameworks for speech recognition, predictive analytics, and interpretation of large sets of unstructured data. Many projects choose CNTK precisely because of its advanced capabilities in the areas of AI and ML.

Apache Mahout

Apache Mahout is an open-source machine learning framework for building massive web applications. This is an exclusive tool for mathematicians, scientists, and statisticians that allows executing algorithms as quickly as possible. It is an excellent choice for developing machine learning applications with performance scalability as it also acts as a distributed linear algebraic framework.

The focus is on filtering, collaborative grouping, and classification, allowing developers to create their mathematical calculations in an interactive environment. The calculation may be performed on a big data platform. Moreover, you can move the same code around in your applications as well as implementations.

It provides a distributed linear algebra and statistical engine that runs and is distributed along with an interactive shell and library for application communications.

Apache Singa

Apache Singa is a machine learning framework that provides a flexible architecture for scalable distributed learning for big data. It runs on a wide range of hardware, so it is expandable. Natural language processing and image recognition act as the main application areas for the platform. Apache Singa supports traditional machine learning models including regression.

This deep learning framework is flexible and versatile, so developers can use it to build web applications for different business areas.

Amazon Machine Learning

AML is a machine learning system that is a set of tools and wizards for building high-tech, intelligent, high-performance applications without having to recode the code.

Amazon Machine Learning provides predictions to the application using the power of easy-to-use APIs. To create new models, you need to perform various operations, including regression, multiclass categorization, and binary classification. In this case, AML can connect your applications to the cloud.


Accord.NET is a machine learning framework for scientific computing based on Dot Net technologies. It provides different libraries for various applications such as statistical data processing, image processing, linear algebra, and neural networks. It has libraries that are available as source, installer, and NuGet packages. Its matrix library offers reusability and slower algorithmic changes.

Final Thoughts

The web development industry is largely driven by the practice of machine learning, which is undoubtedly the fastest-growing field of computer engineering. ML is based on the idea of designing devices and models that can mimic human thinking, as well as solve problems in natural language processing and text analytics. It is the ideal solution for automated actions that require precision and is also capable of handling huge amounts of data. Hence, machine learning in web development is an innovative solution that largely satisfies the needs of businesses and users, improving the quality of customer service.

We at Brights have been successfully and effectively implementing machine learning practices for many years. We take projects to a fundamentally new level and show significant results in web development using advanced ML frameworks. Contact us and our team of experienced developers will offer a solution for your project that will fully meet the needs of your business, industry, market, and consumers.

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