Cloud machine learning still an answer in search of a question

A year ago Eric Schmidt, chairman of Google's parent company Alphabet, predicted on stage at Google's first cloud conference that machine learning would be the basis for every major IPO within five years.

It was a bold statement, since few enterprises without data scientists had embraced the technology. It also signaled how Google planned to use artificial intelligence to wedge its way in the public cloud market.

Fast forward a year later and cloud machine learning and the "democratization of AI" was one of the most talked about subjects at the same conference -- on stage and on the convention floor.

"Definitely machine learning is a theme that's interesting to me," said Jeremy Pollock, product manager at Mashery, an API management company. "Every single pitch, if not every single presentation, had some aspect of machine learning."

Like many of the 10,000 or so attendees at the Google Cloud Next conference held in San Francisco earlier this month, Pollock was intrigued by the potential of cloud machine learning services, but still unsure how his business unit can use them -- or whether the attention is simply that IT pros have glommed on to the latest hype.

"I'm not quite sure it's 'AI for the masses,'" Pollack said. "I suspect that in practice it takes a lot of thinking about what types of questions you want to answer and what problems you want to solve and whether machine learning is a good fit."

And therein lie some of the hurdles ahead for public cloud providers which continue to place huge bets on the technology. Amazon Web Services (AWS), Google and Microsoft Azure have all rushed to make it easier for customers to use services that could potentially anchor massive amounts of data to their platforms, but questions remain about how easily enterprises can adapt to these methods and whether the public cloud is even the best place to do so.

Enterprises, please

Public cloud providers approach artificial intelligence from multiple angles. Some appeal to companies that want to build complex systems, while others aim to ease enterprises' onramp with packaged software. The latter has been in the spotlight lately, through machine learning algorithm and modeling suites, and APIs for uses such as speech and visual recognition.

Data-driven machine learning is very complicated and it's easy to make mistakes throughout the process, said Vivian Zhang, founder and CTO of the NYC Data Science Academy. Companies may feel incredibly stressed as they approach these techniques, which is where cloud providers come in.

"Cross validation, how they can do modeling, how they can automatically tune the model to reach top performance -- those are top priorities," Zhang said. "That's why I see AWS, Azure and Google moving to make package machine learning tools available to enterprises."

In some ways, public cloud providers are well-suited to abstract much of the underlying work that can take hundreds of hours to train. Each of the three major vendors has years of experience with machine learning through other, more prominent parts of their business, whether it's Amazon's retail business, or Google's search engines, or Microsoft's Office suite and Xbox.

"That kind of machine learning, and that scale and complexity, is always going to be a service that somebody big has to deliver," said Anand Krishnan, executive vice president and general manager of cloud at Canonical, the London-based company behind Ubuntu.

The push into machine learning services is also part of a race by cloud providers to go beyond commodity infrastructure as a service and offer as many services as possible, said Charlie Li, chief cloud officer at Capgemini, a global company with U.S. headquarters in New York.

"It encourages more enterprises to move their workloads to the public cloud," Li said. "So whether it's machine learning or IoT, these just happen to be the latest set of services that people demand, and more and more these services become table stakes."

It's too early to say one vendor is far ahead in this market, and much of the innovation is yet to come. But, unquestionably, machine learning has gained traction, particularly in the media and retail industries that depend on analytics to gain an edge, Krishnan said.

"It's absolutely hot, but it's not going into widespread production in the next three to six months," he said. "It takes time to latch on, but there's potentially much more to come. It was academic two years ago, but it's far more mainstream today."

So you've got machine learning -- now what?

Part of the challenge for these providers as they push machine learning services is that they may have an answer to a question enterprises don't yet ask. Familiarity has improved and vendors have pushed to provide more real-life use cases, but clearly there needs to be more education.

"How would someone come at this fresh or even begin to figure out how to solve a problem with machine learning, or, more importantly, classify what kinds of problems it could solve?" asked Rob Harrop, CEO at Skipjaq, a Richmond, England, startup that built a performance optimization service on top of AWS that incorporates machine learning. "There's a big gap in that they don't know what's possible."

Skipjaq uses machine learning as part of its service, but the company downplays its role in their product because of customer fatigue with the term, Harrop said.

Machine learning products, such as some of those tied to IBM Watson, are intended to solve specific problems, but for the most part, such efforts by public cloud providers are still nascent.

Capgemini customers have started to test simple functionality, mostly using machine learning to automate tasks, such as when to shut down a server or to incorporate Amazon's Alexa into operations, Li said.

Complexity and questions in the public cloud

Public cloud providers have pursued more advanced users even as they court enterprise novices. They've added GPU-powered virtual machines tailored to deep learning and embraced open source projects such as TensorFlow and MXNet. There are also a growing number of startups that build services on top of the public cloud with machine learning baked in.