Best Frameworks for Data Science in Web Development

The technology market is extremely competitive and companies are looking to gain an edge through web design and development.

For a web application business to be modern and competitive, it is necessary to correctly assess the competition and online communication trends when creating it. But before you get started, let me talk about the role that artificial intelligence plays in custom web development and present some of the most popular Data Science frameworks in 2022. Thus, every Data Science Course is now including this in its curriculum.

Also Read: Top Seven Benefits of Choosing Java for Web Development

TOP 5 Data Science Frameworks for Web Development

To help you make your choice, view the website https://litslink.com/blog/five-frameworks-to-generate-best-in-class-data-science-projects to show you 5 innovative Data Science frameworks to look out for in 2022.

TensorFlow

Since its inception in 2015, TensorFlow has remained a developer favorite among machine learning frameworks. This is because the creators of the framework, the Google Brain Team, designed it in such a way that it allows developers to immediately use machine learning for numerical calculations in JavaScript or Node.js web application development.

Also, using this framework, you can create web applications using JavaScript and its modules that recognize key aspects of the functionality of various objects and interactions in the real world.

TensorFlow brings Data Science technologies to the front-end and simulates real-time interactions for the user right in the browser.

Companies large and small are using the framework to transform the web development process so that end users can make the most of the power of machine learning and predictive AI.

Model building has a huge impact on the web development process. TensorFlow uses numerous abstractions and powerful libraries to build and train models and then create multi-component technologies.

Apache Mahout

The Apache Software Foundation worked with a team of developers to create Apache Mahout. It is an open-source machine learning framework for building large-scale web applications. Such applications execute various mathematical algorithms for statisticians and data scientists.

Professional web developers recommend the framework for use in 2020 because it improves application performance with distributed linear algebra structures and a mathematically expressive, R-like domain-specific language Scala for distributed algorithms. Additional advantages are scalability, simplicity, and speed.

Mahout uses the Apache Hadoop library to build scalable managed and unmanaged machine learning cloud systems. Mahout can also be used to create engines with recommended functions for performing classification, grouping, and analysis of large amounts of data.

Therefore, web applications using Mahout can divide users into groups based on shopping behavior, classification, and product groupings.

Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit is another open-source deep learning framework. It can be embedded into web applications via high and low-level APIs for creating neural networks. It feeds information to the application through optimizer APIs and offers many components for measuring the performance of neural networks.

If your goal is to create a fast application, then this deep learning framework is ideal. Its functionality, scalability, and speed make bulky solutions a thing of the past. CNTK-based apps do a great job with image, voice, handwriting recognition.

The framework supports convolutional (CNN) and recurrent (RNN / LSTM) neural networks. CNTK is an extensible library that can be used in Python, C #, and C ++ programs, as well as a machine learning tool through its own Model Description Language (BrainScript).

In addition, the framework boasts versions for 64-bit Windows and Linux, which can be downloaded either from compiled binary module packages or from a library on GitHub.

The framework’s support for the ONNX (Open Source Library for Deep Learning Neural Networks) format has a huge impact on the AI and machine learning web development process. It uses AI models with many frameworks, tools, runtimes, and compilers. Therefore, CNTK is one of the best frameworks for speech recognition, predictive analysis, and interpretation of large unstructured data sets.

Many large tech companies use CNTK in development because of its capabilities in these areas.

Caffe2

It is the perfect deep learning framework for those who believe big is small. Caffe2 is worth looking into if you need a flexible framework. For this, as well as for the ease of expansion, developers love it.

The framework improves application performance through extensible code. Best of all, developers only need to know the basics of C ++ source code.

Caffe2 has Python and MATLAB bindings. The application code will not become more cumbersome, because the framework supports the latest code and model standards. For scenarios involving web development and exploratory experimentation, there are out-of-the-box Caffe2 Model Zoo networks.

One of the main advantages of the framework is speed: it can process over 60 million images per day using just one Nvidia K40 GPU.

Various companies use Caffe2 to create image segmentation and classification applications. Caffe2 speeds up the development process mainly through the use of convolutional neural networks.

In addition, if you use Caffe2 Model Zoo with a set of ready-made models, development can do without code at all.

Apache Singa

Last but not least, Apache Singa is a popular deep learning framework for web development. It is quite versatile, so developers can use it to create applications for different areas of business. It has several advantages, not only for application developers but also for their owners.

Apache Singa has many features, including a flexible architecture for distributed learning and three main components:

kernel for memory management;
input/output (IO) component for data analysis;
model for natural language processing and image recognition.

The Apache Singa team is currently working on versions of SINGA-lite and SINGA-easy to make AI and deep learning more accessible and easier for developers.

SINGA systems are currently used in healthcare for the analysis of electronic medical records. Companies from other industries also use SINGA when building applications for different types of data.

Conclusion

You’ve probably already heard about the use of these Data Science frameworks in web development. You can use these frameworks to create stunning web applications that reach users around the world. This will increase your customer base and ultimately lead your business to success.

If you need such services, but do not have the resources to implement them yourself, find a company that adapts the development to your requirements.

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