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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

With businesses increasingly keen on incorporating artificial intelligence into their operations, machine learning - the ability for a system to learn from large data sets rather than following preset rules - offers a number of benefits. This might mean building predictive models for fraud prevention in financial services, for example, or retailers making better recommendations to their customers. 

Google, Microsoft, IBM and AWS all offer machine learning APIs via their respective cloud platforms, making it easier for developers to build services by abstracting some of the complexity of their algorithms. There are also a growing number of open source deep leaning frameworks for data scientists to use at a deeper level.

Earlier this week Amazon announced that it is open-sourcing its deep learning library, Deep Scalable Sparse Tensor Network Engine, (DSSTNE), which is now available on GitHub. And on Thursday, Google opened itsSyntaxNet neural network framework for developers to build applications that can process human language.

Here are some of the top machine learning tools.

1. Machine learning tools: Amazon's Deep Scalable Sparse Tensor Network Engine (DSSTNE)

Machine learning tools: Amazon's Deep Scalable Sparse Tensor Network Engine (DSSTNE)

The open source deep learning library, pronounced 'destiny', allows data scientists to train and deploy deep neural networks using GPUs.  It can be seen as a response to Google's open sourcing of TensforFlow.

DSSTNE was built by the retail giant's engineers to power its recommendations engine that makes product suggestions to the hundreds of millions of customers on its websites each day.

Amazon said: "We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations.

"We hope that researchers around the world can collaborate to improve it. But more importantly, we hope that it spurs innovation in many more areas."

2. Machine learning tools: Amazon Web Services Machine Learning API

Machine learning tools: Amazon Web Services Machine Learning API

AWS launched its Amazon Machine Learning service in Europe last August, with the aim of making it easier for developers of all skill levels to access complex algorithms. The service was built on the technology used by its own internal data scientists.

AWS says its machine learning service can generate billions of predictions a day, tapping into AWS data from services such as RedShift, S3 and its Relational Database Service.

3. Machine learning tools: Google APIs

Machine learning tools: Google APIs
Image: Google

Google has a host of machine learning tools on its Cloud Platform. This includes its popular Prediction API, which allows users to tap the search giant's algorithms to analyse data and predict future outcomes. Google has been adding more APIs to allow users to build their own machine learning-based services, including Speech, Translate and Vision.


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