Machine Learning: The Next Frontier in the Cloud
Big data infrastructure is firmly entrenched as a key component of modern business, but infrastructure alone isn’t the whole story. Big data is, after all, a term coined to describe datasets that presented a very particular problem: they were too large to be effectively stored, managed, or analyzed using traditional methods.
Data analysis and management is a key factor in the growth and continued relevance of cloud services – and it’s a realm where machine learning is poised to make a big difference.
Machine learning is a hard problem in computing, and it’s one that most businesses simply don’t have the resources to take on. Not surprisingly, the key players in the machine learning and artificial intelligence fields are vast, data-focused companies, Google and Amazon among them.
Google has been less successful in Infrastructure as a Service (IaaS) offerings than competitors like Amazon AWS and Microsoft Azure, but the company’s emphasis on innovations in machine intelligence may give them an edge as the field continues to develop. From natural-language search query processing to self-driving cars, Google has a breadth of machine intelligence operations which should lead to continued advancement.
Few companies can compete with Google in terms of ML/AI innovation – but few companies need to. While “cutting edge” may have high value as a buzzword, most businesses don’t need cutting-edge technologies in order to transform their data management or increase efficiency. Simply put, a company doesn’t need to compete with Google in order to harness the power of their algorithms. The cloud offers an opportunity for Google, AWS, and other machine learning powerhouses to lift up other businesses, even as they compete with each other. Because ML/AI is the focus of so few businesses, it can be profitably offered as a service, and that service can go a long way toward making big data accessible across the board.
There is one caveat, however: machine learning seems to thrive best in the cloud. Standalone ML/AI solutions do exist, but they’re frequently less-developed than cloud-based solutions. One factor driving this difference is that the relationship between SaaS/IaaS providers and data-generating businesses goes both ways, in certain respects. It’s easier for cloud providers to develop solutions to complex problems when their solutions are running on complex datasets.
Cloud technologies grow and improve because of the volume of customers using them, and customers continue to utilize cloud technologies because they offer superior resources when compared to standalone solutions.
The moral? Big data may not be married to the cloud, but it’s becoming increasingly inseparable from it. And as cloud-based services become more and more sophisticated, they’ll be able to amplify – and “app-lify” – business processes to an unprecedented degree.
To learn more about big data trends, cloud computing, and machine learning, visit us at thinQ.