Finding insight in oceans of data is one of enterprises' most pressing challenges, and increasingly AI is being brought in to help. Now, a new tool for Apache Spark aims to put machine learning within closer reach. Announced on Friday, Sparkling Water 2.0 is a major new update from H2O.ai that's designed to make it easier for companies using Spark to bring machine-learning algorithms into their analyses. It's essentially an API (application programming interface) that lets Spark users tap H2O's open-source artificial-intelligence platform instead of -- or alongside -- the algorithms included in Spark's own MLlib machine-learning library. Among the highlights of the new software is the ability to run Spark and Scala through H2O's Flow user interface.

Finding insight in oceans of data is one of enterprises' most pressing challenges, and increasingly artificial intelligence is being brought in to help. Now, a new tool for Apache Spark aims to put machine learning within closer reach. Announced Friday, Sparkling Water 2.0 is a major new update from H2O.ai that's designed to make it easier for companies using Spark to bring machine-learning algorithms into their analyses. It's essentially an API (application programming interface) that lets Spark users tap H2O's open-source artificial-intelligence platform instead of -- or alongside -- the algorithms included in Spark's own MLlib machine-learning library. Among the highlights of the new software is the ability to run Spark and Scala through H2O's Flow user interface.

Finding insight in oceans of data is one of enterprises' most pressing challenges, and increasingly AI is being brought in to help. Now, a new tool for Apache Spark aims to put machine learning within closer reach. Announced on Friday, Sparkling Water 2.0 is a major new update from H2O.ai that's designed to make it easier for companies using Spark to bring machine-learning algorithms into their analyses. It's essentially an API (application programming interface) that lets Spark users tap H2O's open-source artificial-intelligence platform instead of -- or alongside -- the algorithms included in Spark's own MLlib machine-learning library. Among the highlights of the new software is the ability to run Spark and Scala through H2O's Flow user interface.

Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. "It would have been extremely hard to just take that and make it open source". But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.

Machine learning is considered by many to be tech's next frontier. To help researchers and developers who might otherwise not have access to machine learning's benefits, Google on Wednesday announced a new distributed version of TensorFlow, the artificial-intelligence engine the Web giant uses to add capabilities such as speech and object recognition to its products. The new version of TensorFlow will let researchers perform large-scale machine learning across hundreds of computers, shrinking the training process for some models from weeks to hours. Google already uses machine learning algorithms to deliver search results, help translate languages and identify objects in photos. Google open-sourced TensorFlow in November, allowing developers to build on its framework, contribute source code and provide feedback.