In this article I will be discussing cloud computing and how it is used to improve the performance of machine learning applications.
The cloud has been a revolutionary concept in the way that data can be processed and analyzed. In the past, machines had to be physically present at the location of the application, which meant that developers had to design their applications with certain constraints in mind. This also meant that different types of hardware had to be kept available for each application, as well as a large amount of maintenance and support. The cloud allows developers to use a single platform to build their applications, which removes those constraints. This concept is known as elasticity. When an application is created it is given a certain amount of resources from the cloud provider. As the need for those resources changes, the application can request more or less from the cloud. The complexity of managing this resource allocation would have been very difficult before the cloud was available.
Machine learning is a rapidly growing field, especially in the field of image recognition. With the introduction of the cloud, there has been an explosion of new applications for machine learning. By making machine learning applications aware of the cloud and its capabilities, they gain a lot of processing power and storage space. To understand just how powerful this can be we can look at something like Google’s object recognition system. When Google launched its object recognition system it was able to identify objects within pictures with 97
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