Blogs How Can Open-Source Framework Help in Machine Learning?

How Can Open-Source Framework Help in Machine Learning?

  • May 31st, 2020
  • Development

There is a new buzzword in the world of technologies. It’s Machine Learning, in short ML! The modalities of this relatively new-found technology have transformed our way of living. Setting a new wind of change, Machine Learning is gradually emerging a mainstream technology. There are multiple factors out there, owing to which, ML has witnessed such an unprecedented growth and popularity. A major reason behind its widespread pre-eminence is the fact that developers can easily apply it, courtesy open-source frameworks.

For someone, who has the faintest idea of framework, it’s essential to know that a framework is a combination of programs, languages and libraries, used in developing apps. So, are you looking to delve into Machine Learning in a more serious way? You would be glad to know that a number of robust and useful frameworks are available out there in the market today. However, picking a high-quality framework for your enterprise operations could be a little challenging, especially if you have a dearth of experience or knowledge regarding open-source frameworks.

To help you make the right choice, we have come up with some amazing options in open-source frameworks, which you may get started with. Make sure to run a quick glance through the followings –

Open_source_framework, Machine_Learning

  • Apache Spark MLlib, Apache Mahout & Apache Signa –

“Omne Trium Perfetum” – it is an old Latin adage which translates to ‘everything that comes in three is perfect.’ The confluence of Apache Spark MLlib, Apache Mahout & Apache Signa has shown how powerful a combination of three frameworks could be. Among these, Apache Signa is generally used for processing natural language and recognizing images. Also, it can easily run over a number of advanced hardware, too.

Apache Mahout is known to provide Java libraries and collection to conduct an assortment of mathematical operations. The last one from this trio is Spark MLlib. Spark MLlib is built with an objective of streamlining the usages of Machine Learning. It helps in bringing together multiple learning utilities and algorithms that include clustering, classification, dimensionality reduction, etc.

  • AML –

AML is the abbreviation of Amazon Machine Learning. Since the past few years, this open-source framework has created quite a buzz among developers worldwide. AML is built with multiple wizards and tools to enable developers to prepare ML models sans having to get acquainted with all the complications of how ML functions.  

Backed by Amazon Machine Learning, you can generate more informed and result-yielding predictions without any hassle. AML will also allow you to collect data from Amazon Redshift, which is known as one of the leading data warehouse platforms in the world.

  • TensorFlow –

By handling even the complex-most language and perceptual understanding tasks, TensorFlow has successfully carved a niche for itself in the field of development. Built on Google Brain Team, this framework is able to carry out thorough researches on deep neural networks and ML.

Time and again, TensorFlow has been utilized in a number of Google’s products, Gmail, online search, and the likes. This Python-based Interfaced framework is adept at handling speech recognition as well. What has brought TensorFlow at the core of discussion is its ability to perform mathematical computations and view data flow graphs. Since, this framework is highly flexible, users can easily write their libraries atop of it without any hassle.

  • Accord.NET –

Another framework that has made a huge splash is Accord.NET. Being a .NET ML formwork, it can work wonders in recognizing patterns, processing images, processing statistical data, and the likes. This apart, Accord.NET can be used in hypothesis tests, statistical data distribution, kernel functions, and the likes.

  • Shogun –

Owing to a multitude of qualitative algorithms, Shogun has turned out to be an extremely handy and user-friendly tool. Written in C++, this open-source framework can structure data to address different ML issues. One of the best things about Shogun is that it can easily run on Linux, MacOS, Windows, and so forth. In addition to this, this framework can support bindings to many other Machine Learning libraries. Such lists of Machine Learning libraries include SLEP, SVMLight, libqp, Tapkee, LibLinear, and the likes.

There are many other open-source frameworks available in the market, which can help you in your ML endeavours. To know more about in this regard, make sure to get in touch with the best of developers in town.


Last updated June 3rd, 2020

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