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14 machine learning tools and frameworks to harness AI

Matthew Finnegan and Christina Mercer | June 27, 2016
More and more businesses are looking at how they can use machine learning in their operations

Its GPU-based Machine Learning platform also helps to create machine learning models, and is integrated with other cloud services such as Google Data Flow and Biq Query.

4. Machine learning tools: Google TensorFlow

Machine learning tools: Google TensorFlow

Google also open sourced its TensorFlow software library through an Apache licence, which powers many of its own services including Google Photos, to Google Cloud Speech and is now being used by its DeepMind division for research.

TensorFlow can produce C++ or Python graphs that can be processed on CPUs or GPUs. These flow graphs depict the movement of data running through a system.

5. Machine learning tools: Microsoft Distributed Machine Learning Toolkit (DMLT)

Machine learning tools: Microsoft Distributed Machine Learning Toolkit (DMLT)

Microsoft's machine learning toolkit - which is available onGithub - aims to ease crowded machine learning clusters, making it easier to run multiple (and differing) machine learning applications at the same time. 

"Bigger models tend to generate better accuracies in various applications," Microsoft said. "However, it remains a challenge for common machine learning researchers and practitioners to learn big models."

6. Machine learning tools: Microsoft Computational Network Toolkit (CNTK)

Machine learning tools: Microsoft Computational Network Toolkit (CNTK)

Also from Microsoft, the Computational Network Toolkitenables users to create neural networks depicted in directed graphs. While primarily made for speech recognition technology, since April 2015 it has become a more general machine learning toolkit supporting image, text and RNN training (recurrent neural network - a type of neural network). 

7. Machine learning tools: IBM Watson Analytics

Machine learning tools: IBM Watson Analytics
Image: IBM

The Watson Analytics cloud service was unveiled in 2014 as part of IBM's plans to turn Watson from a part-time game show contestant into a bona fide enterprise software proposition.

It aims to help organisations that have little or no experience of predictive analytics put their business data to good use.

IBM had already launched its Watson Developer Cloud - in 2013 - offering access to APIs via its Bluemix platform as a service cloud, allowing developers to create their own applications based on Watson's smarts.

8. Machine learning tools: BigML

Machine learning tools: BigML
Etsy.com uses BigML for sales predictions (pictured). Image: BigML

It is not only big IT firms that are moving into artificial intelligence in the cloud. BigML is one of a number of startups in the market aiming to open artificial intelligence to a wider audience.

Founded in Oregon in 2011, BigML offers a simple user interface, allowing users to upload data sets to start making predictions.

 

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