An interesting trend that is helping drive cloud adoption is the rise of machine learning. As organizations seek ways to automate more processes, the use of machine learning is also increasing in order to meet those needs. As machine learning has increased, it has helped to increase the use of the cloud to help store and process the massive amounts of data that can be required for it.

What is machine learning?

Since the invention of computers people have been trying to answer the question of whether a computer can ‘learn’…

Source: Andres Munoz, Courant Institute of Mathematical Sciences

Machine learning is a way for computers to “learn” things without needing to have as much specific programming done as would a typical application. Machine learning can help create algorithms which can use data and either learn from the data or make various types of predictions based on identified patterns in the data. Such learning can be useful for a number of tasks that would otherwise be much more difficult, such as OCR (optical character recognition), email filtering, medical monitoring, text analysis, photo analysis, video analysis, translation, and speech recognition, and many others.

For example, email filtering can be done much more simply, since patterns used by spam or scam emails can be identified and allow for a better chance that those types of messages will be moved to a “spam” folder or blocked entirely. This is certainly useful in helping to keep inboxes free of messages most users do not want to sift through on their own every day!

Machine Learning and Cloud Innovation

Image depicting machine learning as the focal point of the cloud. Source: Forbes

As noted in the image from Forbes, there are a number of things that are driven by machine learning that also helps to drive cloud innovation. Things such as business intelligence, personal assistants, IOT (Internet of Things), bots, and cognitive computing are brought about by machine learning, which in turn allows for the cloud to be a desirable place to collect, store, analyze and retrieve the data needed for these various applications.

For instance, IOT is big on connecting machines so that they can communicate with one another and exchange data. Machine learning helps to drive these types of interactions, and using the cloud makes it even easier for machines to exchange data with one another as there will be an easy way to make those connections.

As a result, the cloud has seen much innovation in its ability to handle this type of data exchange as cloud systems have become more flexible and offer the ability to scale much more easily than with a traditional data center.

Cloud infrastructure and scalability

Communications with remote systems across factories and transportation, real-time data gathering and analytics, and the ability to integrate with enterprise software that drives your business are often requirements of any IoT system that promise value in additional automation, value discovery, cost savings, and new ways to improve customer service.

Source: Tracy Siclair, HP Enterprise

Machine learning and cloud services make an excellent combination, as many cloud services, such as Morpheus, make it easy to provision the resources needed for the collection, storage, and retrieval of large amounts of data. The biggest reasons this works is that such cloud services offer both flexibility and scalability.

A cloud service is typically flexible enough to allow you to provision servers with different specifications, depending on the needs of the various pieces required for the machine learning that needs to occur. For example, if you need different operating systems for different servers, it is as simple as selecting them and spinning up the server.

The same applies to provisioning various different databases such as MySQL, MongoDB, and so on – you can simply spin up what you need easily and move on to any setup and programming that needs to be done without worrying about acquiring hardware.

Scalability is another big reason that machine learning and the cloud work well together. Since machine learning often needs a progressively larger amount of storage space, using a cloud service makes a lot of sense.

With a standard data center, you can end up spending a great deal on what you determine the maximum amount of storage space is that you will need up front, with the potential to still need more later. This can be quite costly both in the beginning and as time goes on.

On the other hand, a typical cloud service allows you to purchase only the storage space you need up front, then scale up to more space as you need it. This definitely saves money in the beginning and allows you to add more space as needed rather than simply jumping to an incredibly large amount of space before you need it. As you can see, this would definitely help to drive cloud adoption!