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Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

Machine Learning is rapidly becoming the mainstream and changing the way we carry out tasks. While many factors have contributed to this current boom in machine learning, the most important reason is the wide availability of open source frameworks.

’Open source’ refers to a program that is created as a collaborative effort in which programmers improve the code and share the changes within the community. Open source sprouted in the technological community in response to proprietary software owned by corporations. The rationale for this movement is that programmers not concerned with proprietary ownership or financial gain will produce a more useful product for everyone to use. 

Framework: It refers to a cluster of programs, libraries and languages that have been manufactured for use in application development. The key difference between a library and a framework is ‘’inversion of control’’. When a method is summoned from a library, the user is in control. With a framework the control is inverted- the framework calls the user.

If you are plunging full-fledged into machine learning, then you clearly need relevant resources for guidance. Here are the top 5 frameworks to get you started.

  1. TensorFlow:

TensorFlow was developed by the Google Brain Team for handling perceptual and language comprehending tasks. It is capable of conducting research on machine learning and deep neural networks. It uses a Python-based interface. It’s used in a variety of Google products like handling speech recognition, Gmail, photos and search.

A nifty feature about this framework is that it can perform complex mathematical computations and observe data flow graphs. TensorFlow grants users the flexibility to write their own libraries as well. It is also portable. It is able to run in the cloud and on mobile computing platforms as well as with CPUs and GPUs.

  1. Amazon Machine Learning (AML):

AML comes with a plethora of tools and wizards to help create machine learning models without having to delve into the intricacies of machine learning. Thus it is a great choice for developers. AML users can generate predictions and utilize data services from the data warehouse platform, Amazon Redshift. AML provides visualization tools and wizards that guide developers. Once the machine learning models are ready  AML makes it easy to obtain predictions using simple APIs.

  1. Shogun:

 Abundant in state-of-the-art algorithms, Shogun makes for a very handy tool. It is written in C++ and provides data structures for machine learning problems. It can run on Windows, Linux and MacOS. Shogun also proves very helpful as it supports uniting with other machine learning libraries like SVMLight, LibSVM, libqp, SLEP, LibLinear, VowpalWabbit and Tapkee to name a few.

  1. NET:

Accord.NET is a machine learning framework which possesses multiple libraries to deal with everything from pattern recognition, image and signal processing to linear algebra, statistical data processing and much more. What makes Accord so valuable is its ability to offer multiple things which includes 40 different statistical distributions, more than 30 hypothesis tests, and more than 38 kernel functions.

  1. Apache Signa, ApacheSpark MLibApache, and Apache Mahout:

These three frameworks have plenty to offer. Apache Signa is widely used in natural language processing and image recognition. It is also adept in running a varied collection of hardware.

Mahout provides Java libraries for a wide range of mathematical operations. Spark MLlib was built with the aim of making machine learning easy. It unites numerous learning algorithms and utilities, including classification, clustering, dimensionality reduction and many more.

 With the advent of open source frameworks, companies can work with developers for improved ideas and superior products. Open source presents the opportunity to accelerate the process of software development and meet the demands of the marketplace.

Boost your machine learning endeavors by enrolling for the Apache Spark training course at DexLab Analytics where experienced professionals ensure that you become proficient in the field of machine learning.


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Latest Open Source Tools in Data Analytics Beyond Apache Spark

Latest Open Source Tools in Data Analytics Beyond Apache Spark

In the IT world change is always in the air, but especially in the realm of data analytics, profound change is coming up as open source tools are making a huge impact. Well you may already be familiar with most of the stars in the open source space like Hadoop and Spark. But with the growing demand for new analytical tools which will help to round up the data holistically within the analytical ecosystem. A noteworthy point about these tools is the fact that they can be customized to process streaming data.

With the emergence of the IoT (Internet of things) that is giving rise to numerous devices and sensors which will add to this stream of data production, this forms one of the key trends why we need more advanced data analytics tools. The use of streaming data analysis is used for enhanced drug discovery, and institutes like SETI and NASA are also collaborating with each other to analyze terabytes of data, that are highly complex and stream deep in space radio signals.


The Apache Hadoop Spark software has made several headlines in the realm of data analytics that allowed billions of development funds to be showered at it by IBM along with other companies. But along with the big players several small open source projects are also on the rise. Here are the latest few that grabbed our attention:

Apache Drill:

This open source analytics tool has had quite good impact on the analytics realm, so much so that companies like MapR have even included it into their Hadoop distribution systems. This project is a top-level one at Apache and is being leveraged along with the star Apache Spark in many streaming data analytics scenarios.

Like at the New York Apache Drill meeting in January this year, the engineers at MapR system showed how Apache Spark and Drill could be used in tandem in a use cases that involve packet capture and almost real-time search and query.

But Drill is not ideal for streaming data application because it is a distributed schema free SQL engine. People like IT personnel and developers can use Drill to interactively explore data in Hadoop and NoSQL databases for things such as HBase and MongoDB. There is no need to explicitly describe the schemas or maintain them because the Drill has the ability to automatically leverage the structure which is embedded in the data. It is capable of streaming the data in memory between operators and minimizes the use of disks unless you need to complete a query.


Both big and small organizations are constantly working on new ways to cull actionable insights from their data streaming in constantly. Most of them are working with data that are generated in clusters and are relying on commodity hardware. This puts a premium label on affordable data centric work processes. This will do wonders to enhance the functionality and performance of tools such as MapReduce and even Spark. With the open source project Grappa that helps to scale the data intensive applications on commodity clusters and will provide a new type of abstraction which will trump the existing distributed shared memory (DSM) systems.

Grappa is available for free on the GitHub under a BSD license. And to use Grappa one can refer to its quick start guide that is available readily on the README file to build and execute it on a cluster.

These were the latest open source data analytics tools of 2017. For more such interesting news on Big Data analytics and information about analytics training institute follow our daily uploads from DexLab Analytics.


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