By AI Trends Staff
Enterprises are engaging in a multi-cloud strategy to distribute their AI across the different capabilities of the cloud providers.
Wells Fargo for instance has made multi-cloud a key part of its strategy. “You’re going to pick on the provider who has differentiated themselves and at that point it becomes — it really has to become part of your strategy to be agile to move that workload across from one provider to the other,” said Mike Telang, executive vice president and head of enterprise architecture at Wells Fargo,at the recent VMWorld 2019 in San Francisco, quoted in ZDNet.
The cloud strategy at Wells Fargo is a lot about where the data goes. “When we think of workload we typically think about applications, but for us, I think the value will be in the ability to move our data from one to the other without leaving that data behind,” Telang said. “So our cloud strategy, when we think of multi-cloud, we’re really trying to kind of simplify it by saying, ‘What’s our strategy around SaaS, what’s our strategy around PaaS, what’s our strategy around IaaS’ — where we see the big data go, so we’re looking at it that way.”
Wells Fargo has applied AI and machine learning to help protect against fraud and money laundering. At the same time, regulators want to know how decisions are made about providing credit or loans, to try to ensure all customers are treated fairly. Telang called this a “double-edged sword” for banks.
Following the Data in a Multi-Cloud World
In today’s multi-cloud world for large enterprises, AI tools are able to access and manage an endless amount of data. Companies that have applied AI tools to cloud silos of data may have to be retooled to fit a multi-cloud infrastructure with AI at its core, suggested Subram Natarajan, chief technology officer of IBM India, in a recent article published in Tech2/First Point.
While a one-cloud-fits-all approach might have looked appealing in the early days of cloud computing, in reality business functions and IT arms of organizations picked and chose a mix of public and private clouds for their data centers, depending on the business need. The hybrid multi-cloud strategy has proven successful for many companies, for cost effectiveness and the ability to develop and deploy applications quickly. Also, the strategy can cater to the unique requirements of business units at different stages of maturity. Concerns about security and data governance can be addressed at a business unit level as well as company-wide.
The more relevant data available to AI systems the better, so multi-cloud is consistent with AI rollouts. “AI in a multi-cloud infrastructure helps developers, who can leverage the building blocks of technology that exist within respective cloud environments,” Natarajan said.
Research from Ovum show that while 20 percent of business processes have moved to the cloud, 80 percent of mission-critical workloads are still running on-premise, due to performance and regulatory requirements. Business managers and IT professionals are finding that moving data and applications across different on-premise and cloud computing infrastructures is difficult. The ideal is for data to flow seamlessly and be able to interact across clouds to realize greater value.