3 Popular Open Source Machine Learning Frameworks for ML Aspirants
3 Popular Open Source Machine Learning Frameworks for ML Aspirants

A good majority of companies offering software development services in Dubai rely on open source technologies for various purposes. There are many reasons for this. For years, open source technologies has been a major contributing factor in the growth of many industries, including software development. 

Today, technologies have advanced to a different league – into the likes of Artificial Intelligence, Robotics, Machine Learning, and Blockchain etc. Out of the hottest technologies present today, Machine Learning and AI seem to be offering the most potential for businesses. Open source contributes here as well, in the form of frameworks and tools to leverage these technologies efficiently. 

Modern organizations offering open source development services can now leverage cutting-edge machine learning frameworks to build powerful, sophisticated solutions that cater to changing business needs in a dynamic ecosystem. 

That said, here are 3 of the most popular open source Machine Learning frameworks every ML aspirants should know about. 


The Shogun project was initiated in 1999. Over the years, a large number of programmers worked on it transforming it into a free library useful for ML practitioners. It’s written in C++ and runs on Windows, Linux, and macOS.

Shogun is designed for large-scale learning for a wide range of feature types and settings including regression, classification, clustering etc. Shogun’s popularity is mainly because of the fact that it contains a plethora of top-notch algorithms including efficient SVM implementations, kernel hypothesis testing, Krylov methods etc. In addition, it also supports integration with other ML libraries like LibSVM, LibLinear, SVMLight, libqp etc. 

Apache Mahout

Another free, open source project of the Apache Software Foundation, Apache Mahout was designed to develop free, scalable ML algorithms for many areas such as clustering, collaborative filtering, and classification. It’s built on top of Apache Hadoop using MapReduce paradigm. Its biggest advantage is when big data is involved. Once the Hadoop Distributed File System stores enough big data, Mahout will provide the tools to automatically identify meaningful patterns in those data sets quickly and easily. 


TensorFlow was developed by the Google Brain Team to perform a variety of tasks. It’s essentially an open source ML library that allows one to conduct advanced research on machine learning and deep neural networks. TensorFlow is what’s behind smart Google apps like Gmail, speech recognition, Google Photos, and even the search engine itself. It’s portable, supports Docker, and enables can show visualizations and data flow graphs to users.


Till now, open source was mainly popular in the Middle East’s IT industry due to its merits in full stack development as LAMP development services still have great demand in Dubai. Considering the fact that the future of the world would be centered on interconnected devices and artificial intelligence, businesses would be wise invest once more in open source technologies to figure out and hopefully leverage AI and ML.