Amazon has announced Amazon Machine Learning, a cloud-powered service aimed at giving developers access to machine learning without requiring them to learn complex algorithms.
In an Amazon blog post, Jeff Barr, chief evangelist at Amazon Web Services (AWS), explained that Amazon Machine Learning simplifies the process of injecting machine learning into applications.
"You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale," he said.
"You can benefit from machine learning even if you don't have an advanced degree in statistics or the desire to set up, run and maintain your own processing and storage infrastructure."
Amazon Machine Learning enables developers to plug it into their apps through the use of simple application programme interfaces (APIs) rather than needing to hard-code the machine learning capabilities into their applications.
As a cloud-powered service sitting on top of AWS, Amazon Machine Learning can be quickly deployed into apps as a pay-as-you-go tool that allows developers to scale their use of machine learning up or down, the company said.
The service is derived from Amazon's own machine learning technology, which the company uses to manage its supply chain and catalogue, along with detecting fraudulent transactions.
Amazon's foray into providing machine learning as a service will likely see it encounter stiff competition from the likes of IBM and Microsoft.
IBM recently opened its cloud-based Watson Analytics service to give anyone access to its machine learning technology, which makes use of cognitive computing and understands natural language.
Microsoft's Azure Machine Learning is the Redmond company's take on cloud-powered machine learning designed to deliver predictive analysis through data mining and artificial intelligence.
Amazon also announced its Elastic File System (EFS) for AWS, due for release later in the year. The service has been designed to provide multiple virtual servers running in Amazon's Elastic Compute Cloud (EC2), with access to a centralised managed file system.
"We expect to see EFS used for content repositories, development environments, web server farms, home directories, and big data applications, to name just a few," said Barr. "If you've got the files, we've got the storage!"
Much like Amazon Machine Learning, EFS also uses simple AWS interfaces and APIs to allow the service to be quickly set up by users.
Based on solid state hard drive architecture, EFS is another AWS service that users can quickly grow or shrink depending on their file storage needs.
This means customers pay for what they use, at a rate of $0.30 per gigabyte, rather than get tied into contracts and stringent service agreements.
However, as organisations find themselves with increasing amounts of digital data, attractive storage costs could rapidly escalate.
The cloud storage market is a particularly competitive arena, with providers under-cutting each other on a regular basis. Google recently unveiled Cloud Storage Nearline offering data storage at a tiny rate of one cent per gigabyte.
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