"Our goal is to create applications that can see, hear and understand," says Jeff Dean, senior fellow at Google and one of leaders of the firm's machine learning strategy.
Artificial intelligence is a clear priority for Google. Last week, its AlphaGo program defeated world Go champion Lee Sedol, while its DeepMind arm is considered to be at the forefront of deep learning research. Machine learning also underpins many of its well-known products, from YouTube to Google Translate.
And now Google is increasingly opening its technology up to other businesses as it attempts to grow its cloud operations.
At its Cloud Next conference Google unveiled the Cloud ML service which helps machine learning engineers build "sophisticated, large" models based on its TensorFlow deep learning library, which was opened sourced last year.
Google also added to its list of 'pre-trained' machine learning platforms aimed at programmers without data science skills.Cloud Speech allows developers to convert audio to text by "applying powerful neural network models in an easy to use API", the company says, and joins its Translate and Cloud Vision APIs.
How can businesses use machine learning?
Speaking to press at Google's cloud user conference, Dean said that its machine learning tools will "allow other companies to build the same kind of understanding and insight from their data" that Google has achieved internally.
This could be applied in retail, for instance.
"For example, if you have some data about customers and transactions and you want to predict something about it, that is a very general setting that can be used in all kinds of different enterprises," Dean said.
"So you could predict which customers are going to buy more than a hundred dollars of stuff and send them an offer."
Online retail logistics firm Ocado Technologies already uses machine learning algorithms in various ways across its business, such as powering its robotic systems. General manager James Donkin, says that the company has been evaluating TensorFlow to help it build new algorithms, and would welcome the ability to move workloads to the cloud.
"We really like the fact that we can run it locally, but if we want to use it on a larger data volume it is easy to move it to the Google cloud to run the same TensorFlow code," he said, adding that Google's decision to open source the framework is "a big plus". "We don't like black box technologies that we can't look inside," he said.
Democratising machine learning access
Dean says while the TensorFlow and Cloud ML tools are aimed at more sophisticated machine learning use cases, the cloud-based APIs cater to a wider audience of developers.
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