The Cloud AI and Machine Learning as a Service

machine learning as a service

Cloud computing and remote technologies are enabling so many opportunities in regards to data storage, processing, and analytics, the list is seemingly endless. These technologies have also enabled the rise of bigger, more robust data collection policies and procedures.

In 2008, Google was processing 20,000 terabytes of data per day. That number has grown. Furthermore, over 2.7 Zettabytes of data exists in the digital universe today. These stats are positively insane, but they are nothing more than a drop in the hat compared to what will be in ten years’ time.

Data is being created at alarming rates, and we need better, faster tools to make sense of it all. That’s exactly where machine learning and AI platforms come into play.

Machine learning and AI can be leveraged to process, store, and manage data at incredible speeds; near instantaneously to be exact. The technology is so lucrative that many companies and organizations have already begun implementing AI across a variety of mediums and channels including voice, vision, language and more.

According to Accenture, 85% of business and IT executives expect to deploy or make significant investments in AI-related technologies over just the next three years.

One of the more beneficial implementations—and cost-effective—of AI and machine learning for modern organizations is via the cloud, as a service type opportunity. In fact, it’s completely altering the way companies use and leverage their first, second-party, and various external types of data.

Cloud AI and Machine Learning as a Service

AWS Deep Learning, Microsoft Azure AI Platform, Oracle Cloud, Google’s Cloud Machine, and IBM’s Watson, these are just a few of the major cloud AI and machine learning platforms currently available.

It’s a relatively new concept that has a lot of parallels to modern cloud computing configurations such as SaaS, DaaS, and IaaS. The processing, hardware resources, maintenance, and even necessary workforce are all handled remotely by a service provider. This leaves organizations such as yours free to ripe the benefits of the technology, without having to maintain entire systems and configurations in-house.

What really makes Cloud AI and machine learning so enticing, are the benefits received by outsourcing the necessary systems. AI and machine learning systems can be incredibly costly. It takes time to develop these tools and software applications, including various testing and revision periods. An AI system isn’t simply turned-on in an accurate, reliable state. Algorithms, foundations, and machine learning techniques must be deployed to help the system become accurate and optimized.

Furthermore, the repair and maintenance of such systems, along with maintaining optimal conditions and effectiveness, can also be resource intensive.

But outsourcing the system to a third-party provider means you don’t have to worry about most of those elements. Instead, it is your provider’s responsibility to maintain an operational, reliable, and secure system for use.

The Many Benefits of Artificial Intelligence and Machine Learning

In general, most of the pros or benefits you will see when implementing machine learning and AI platforms are the same anyone else will. This is because, by nature, the technology is already vastly improved over legacy configurations. Imagine a system that can be autonomous, operational every hour of the day and night, and provides instant results; what we refer to as “real-time” in the business world. And all of that can be had, quickly, without deploying much hardware at your local properties, and without diverting your existing workforce and hiring new professionals.

That is exactly the scenario and functionality modern AI and machine learning systems can offer your organization.

It’s why IBM’s huge bet on AI software-as-a-service through Watson has and will pay off considerably for the company. The physical and digital restrictions of maintaining such systems in-house are offset simply by using a third-party provider. You get all the bells and whistles, without the hassle, so to speak.

Companies like Paypal are using machine learning to fight fraud, CAD is being used in healthcare to make early diagnoses, and retailers like Wal-Mart use the technology to better understand their customers.

You’ll notice that in all these examples, the companies in question do not specialize in modern technology, cloud computing, and machine learning platforms. Yet, they are able to leverage the technology as if they do maintain such systems. Thank you cloud computing and modern remote technologies!

The question then becomes, when is the right time to implement or adopt such technologies within your own organization. The best answer is as soon as possible. Thanks to modern setups, you no longer have to worry about—or deal with the burdens—of operating the necessary hardware within a local data center. Instead, it can all be outsourced, allowing you to reap the rewards and benefits, devoid of the significant costs and drawbacks of a local AI or machine learning setup.

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Nathan P. Sykes

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