Web Development: The Top 7 Machine Learning Frameworks

Machine learning, which has proven to be a breakthrough technology of our generation, had its roots in the 1950s. The phrase “machine learning” was invented by Arthur Samuel, an IBM computer scientist, in 1959. He came up with a way to teach computers how to play the game of Checkers.

A significant contribution to this field was made in the 1960s by Alan Turing’s research. The Nearest Neighbour algorithm was created in 1967 as more practical use of machine learning. As the first pattern recognition system, it assisted salespeople in finding the most direct route to their desired location(s).

In today’s world, artificial intelligence (AI) is the driving force behind concepts such as self-driving vehicles, natural language processing, and dynamic pricing, to mention a few examples. It is altering the way the internet works.

We’ll take a look at how machine learning is influencing web development in this post. There are a number of different frameworks that can be used to implement machine learning in web development. If you’re a software developer, you’ll benefit from understanding more about machine learning.

Machine Learning’s Importance in Web Development

Employing scalable solutions that can be modified in response to changing client demands has been imperative in recent years. This revolution in web development necessitates the usage of cognitive technologies like machine learning.

Let’s take a look at one practical use case of machine learning to see how it relates to our daily lives. When we buy things online, on-demand entertainment platforms tell us which movie to watch, and food delivery services give us the best meal options, we don’t have to think about what to eat anymore.

Personalized web development at this level demonstrates the critical role that machine learning will play in the future of web development.

There are a lot of ways to use personalization, but it’s just one. Web development can benefit from machine learning in a variety of ways, including chatbots and content generation. As technology advances, we should expect to see more and more applications of machine learning in the near future.

Machine Learning Frameworks

Good research is needed before selecting a machine learning framework for web development. Choosing which machine learning framework to use is highly dependent on the goals of the website in question, as well as the web development frameworks that will be used.

Machine Learning

7 machine-learning frameworks that are extensively used today and will be for a long time to come are summarised in this section.


For the first time in 2015, TensorFlow was released to the general public by the Google Brain team. It is an open-source software library that supports Python, C, C++, Java, and Go programming languages.

Neural networking, natural language processing, speech/text recognition, and forecasting are some of the applications of TensorFlow. People use it to do things like translate things on Google. It’s also used by Twitter and IBM and many other companies.

Allowing for the creation of sophisticated models, Tensorflow’s flexible design and powerful libraries make it an ideal platform. It has also earned a position among their favorites because it allows developers to create web apps using JavaScript.

Cleaner code is easier to read, write, and debug if you use a single high-level API. This makes it easier to work with CPUs or GPUs. Overall, it introduces machine learning to the frontend, providing users with real-world experience via web browsers.

Microsoft Cognitive Toolkit

Cognitive Toolkit (CNTK) was released in 2016 by Microsoft Research. It’s a C++-based open-source deep learning framework with Python interface support. Speech recognition, predictive analysis, and the analysis of massive unstructured datasets are all common uses for CNTK. Parallel execution and automated differentiation are among the characteristics it enables.

Because of its scalability and functionality, CNTK eliminates the need for cumbersome, large-code solutions. Because of its ease of use, it has become a popular choice for those who want to learn new skills. Another notable characteristic of the CNTK framework is its support for convolutional and recurrent neural networks.

Apache Mahout

Developed by the Apache Software Foundation, Apache Spark is an open-source machine learning framework. Java-based Apache Mahout is primarily intended for use by mathematicians as well as to statisticians, data scientists, and machine learning researchers. Mahout, which is a distributed linear algebra framework, has a reputation for having excellent speed.

For cross-platform development, Mahout is written in Java and Scala. Extensive use is seen in recommendation-based engines where enormous volumes of data need to be grouped and categorized. Due to its ability to classify groups based on purchase patterns, product features, and other factors, Mahout-based web development is more popular.


Caffe2 is a deep learning framework written in C++ and Python that was released by the Berkeley Vision and Learning Center in 2017. In order to get started developing with Caffe2, you only need a basic understanding of C++ programming.

Caffe2’s extensible code improves app performance, and support for sophisticated models ensures that app code is simple. It supports web development using Python and Matlab bindings, as well as preset networks. No-code development can also be done with the help of pre-trained models.

Image segmentation and classification are common uses for the framework in website development. CNN ensures a quick and effective development process.

Apache Singa

In terms of machine learning frameworks, Apache Singa is an excellent choice because of its combination of speed, security, and popularity. It was released in 2015 by the Apache Software Foundation. It was designed to enable an abstraction-based programming style at first, but thanks to its flexible architecture, it can now handle synchronous, asynchronous, and hybrid techniques as well.

This framework, known as Singa, is extremely adaptable and may be used in a variety of various contexts and domains. In order to examine electronic medical record data, Singa is used by a number of healthcare solutions. Image recognition and natural language processing are two further uses for this technology. These upgrades will make machine learning more accessible by making it easier to use Singa-Lite and Singa-Easy currently.


A machine learning framework called PyTorch was widely praised when it was released in 2016. PyTorch, an open-source machine learning library for Python based on Torch, was created under Facebook’s AI department (another machine learning framework). Because its algorithms follow the Python control flow, Python programmers have no trouble using it.

Natural language processing is where PyTorch is most commonly employed. PyTorch is regarded as one of the top machine learning frameworks due to its high performance, efficient GPU utilization, and prepackaged data training models.


Chainer, an open-source deep learning framework built on Python’s Numpy and CuPy libraries, was developed by a Japanese business in collaboration with IBM, Intel, Microsoft, and Nvidia. Speech recognition, machine translation, and sentiment analysis are the most common applications.

During training, Chainer uses a “Define-by-Run” method. This means that the connections in a network are not already set in stone but are made as the training progresses. Chainer is the go-to framework for designing large-scale systems due to its rapid performance and ease of debugging.


Machine learning is thriving in our present digital reality, which is an irrefutable fact. For this to be possible, we must first take full use of today’s lower costs for machine learning hardware and storage, as well as the vast amounts of data that can be used to train these models in the wild.

Machine learning, as we’ve seen, has a lot of advantages. Customer activity and behavior can be analyzed to uncover customer expectations and improve the user experience. Additionally, it uses automation to enhance data mining, discovers trends more quickly, and automate responses based on observations.

With machine learning, websites may be tailored to the needs of each individual client in order to assist them to achieve their goals in the most efficient and effective manner possible. Machine learning also builds robust security checking mechanisms and protects websites from ever-changing security threats.A combination of machine learning and web development will have a profound impact on the future of the digital world. User expectations and corporate needs will be met by incorporating machine learning algorithms into web development services. When it comes to developing with this revolutionary technology, the possibilities are virtually limitless, and the ramifications will definitely be life-changing! It’s also wise to hire a Machine Learning developer to assist with the development of an application, which will allow you to expand your business even further.

Harin Panchal. I am a Content Strategist at Aelius Venture Pvt Ltd