An ML showdown in search of the best tool

An ML showdown in search of the best tool

  • May 4, 2019
Table of Contents

An ML showdown in search of the best tool

Ever burgeoning digital data combined with impressive research has lead to a rising interest in Machine Learning or ML, which has further powered a vibrant ecosystem of technologies, frameworks, and libraries in the space. Scikit-learn sees high adoption from the tech community. The most probable reason is a powerful Python interface that allows tweaking of models across multiple parameters.

MLlib and H2O should be considered when working with Spark. Spark does come with MLlib and has a higher level wrapper called SparkML that supports the same.

In some cases, H2O offers a suitable solution and can be used alongside MLlib or added when needed. Weka’s interface makes it the easiest to use but, it isn’t popular within the tech community.

Interestingly, one should also note that Weka offers additional data mining features. PyBrain’s development has been discontinued apart from 10 small bug fixing commits over the last two and a half years.

MLlib and H2O should be considered when working with Spark. Spark does come with MLlib and has a higher level wrapper called SparkML that supports the same. In some cases, H2O offers a suitable solution and can be used alongside MLlib or added when needed.

Weka’s interface makes it the easiest to use but, it isn’t popular within the tech community. Interestingly, one should also note that Weka offers additional data mining features. PyBrain’s development has been discontinued apart from 10 small bug fixing commits over the last two and a half years.

Source: thoughtworks.com

Tags :
Share :
comments powered by Disqus

Related Posts

DeepMind and Google: the battle to control artificial intelligence

DeepMind and Google: the battle to control artificial intelligence

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: “So today I’m going to be talking about different approaches to building…” He stalled, as though just realising that he was stating his momentous ambition out loud.

Read More
Hash Your Way To a Better Neural Network

Hash Your Way To a Better Neural Network

The computer industry has been busy in recent years trying to figure out how to speed up the calculations needed for artificial neural networks—either for their training or for what’s known as inference, when the network is performing its function. In particular, much effort has gone into designing special-purpose hardware to run such computations. Google, for example, developed its Tensor Processing Unit, or TPU, first described publicly in 2016.

Read More
Introducing Ludwig, a Code-Free Deep Learning Toolbox

Introducing Ludwig, a Code-Free Deep Learning Toolbox

Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support, object detection, improving maps, streamlining chat communications, forecasting, and preventing fraud. Many open source libraries, including TensorFlow, PyTorch, CNTK, MXNET, and Chainer, among others, have implemented the building blocks needed to build such models, allowing for faster and less error-prone development.

Read More