Google Cloud's Latest Research on Machine Learning: Getting Started
Google Cloud’s newest report on machine learning reveals the advantages of harnessing such technology for your business. It’s clear from the research that machine learning is catching on fast, and early adopters are already noticing the many benefits. So, the results are there, but how does one get in on this revolutionary way of doing business?
Well, Google acknowledges that adopting machine learning is no easy feat. The report admits that machine learning “typically requires elastic computing resources, massive processing power, and deep expertise.” Deloitte Access Economics found that depending on the project, implementing machine learning can cost anywhere from a few hundred dollars to a few million dollars. Fortunately, these projects have garnered a reputation of generating an ROI two to five times their cost.
Such powerful investments often require vast resources. So, how are businesses adopting machine learning and not finding themselves scrambling to complete their projects? Google finds that in this case, everyone is turning to the cloud. Cloud providers offer “not only scalable virtual machines and data storage but also managed services and application programming interfaces.” Thus, research finds the cloud’s capability to “make machine learning accessible to all” is critical to a successful implementation.
Google’s recent report affirms that along with the rise of machine learning comes a similar rise in cloud computing. Supported by increasing confidence in cloud security, the cloud is turning out to be the preferred platform for implementing machine learning.
Statistics demonstrate that our fear of cloud technology, ominous name and all, is fading away. In fact, many have come to consider the cloud a safer place to work. In a survey conducted by Google and MIT SMR Custom Studio, 74% of respondents said they have become more confident in cloud security than they were two years ago.
Google’s newest research compilation suggests that 87% of machine learning workloads will be deployed in the cloud by the end of 2019. Meanwhile, the prominence of big-data in the face of a shortage of experienced analysts calls for “technology that empowers rather than overwhelms,” and the collaboration possible through cloud technology perfectly fits the bill.
IT Tested, Executive Approved
The most recent research report finds that IT and business executives alike are finding compelling reasons to deploy their machine learning work to the cloud. 41% cite the ability to integrate with new tools and platforms. 45% turned to the cloud out of the need for agility and speed to market. The cloud also presents an opportunity for increased flexibility in business processes and vendor choice.
34% reported utilizing the cloud for cost savings. In fact, a Google study in conjunction with Harvard Business Review showed 64% of business leaders were influenced by reduced costs when it came to investing in cloud computing for machine learning. Additionally, these leaders are also finding the cloud a more secure place to work.
So far, Google finds that businesses have benefited greatly from migrating their machine learning processes to the cloud, much to the delight of the aforementioned executives. 70% reported more efficient work processes, which undoubtedly save time and money even as resources are invested in the machine learning process. The time-saving power is evident for the 60% that reported improved productivity. Other benefits included faster time-to-market and improved customer experience.
Business’ Newest Most Powerful Duo: Cloud and AI
Fei-Fei Li, the chief scientist of machine learning and AI at Google Cloud, admits that “AI remains a field with high barriers,” because it requires such rare “expertise and resources.” That’s why Google Cloud is investing in user-friendly tools so that every cloud customer can begin to reap the benefits of machine learning. This research demonstrates just the beginning of a great migration to the cloud where there’s room for everyone aboard the ML train.