Dreamworks uses AIOps to Smooth Out Big Rendering Jobs

“Any sufficiently advanced technology is indistinguishable from magic,” novelist Arthur C. Clarke once quipped. 

But successful advanced technology use cases always are concrete, and add value because real business problems are solved. Consider the way Dreamworks uses AIOps. As a digital content business, Dreamworks arguably has an easier time than most switching to fully-remote work. 

But even so, there are real problems AIOps helps Dreamworks solve. The way the studio schedules workloads if a case in point. One specific problem is ensuring that the internal computing network does not crash when huge rendering jobs are executed, such creating 150,000 animated people in a crowd scene.

Doing all the rendering at once would affect computing performance. 

"We don't want the artists noticing that something's performance has changed," says Skottie Miller, technology fellow and vice president of platform and services architecture at DreamWorks. "We want our synthetic transaction and monitoring framework to tell us before the artists notice that something is trending in a bad direction.”

"It used to be there would be an issue and maybe an engineer noticed because they were looking for it, or maybe the system sent an alert and an engineer would go investigate it," Miller notes. "Now an issue surfaces almost always with a recommendation and, in many cases, a solution before the engineer is in the loop.”

“It lets us run with 24x7 support with fewer sets of eyeballs staring at the systems,” he says. And that is precisely the sort of use case AIOps was envisioned to support: automating the alerting process and preparing a solution without information technology staffs having to do so manually. 

Telecom AIOPs

In the connectivity business, potential applications include network operations monitoring and management, predictive maintenance, fraud mitigation, cybersecurity, customer service, marketing virtual digital assistants, customer relationship management preventive maintenance and battery optimization, for example. 

In the network operations monitoring area, that might include anomaly detection for operations, administration, maintenance and provisioning (OAM&P), performance watching and optimization, alert or alarm suppression, bother price ticket action recommendations, automated resolution of bother tickets, prediction of network faults or congestion prediction, for example. 

Artificial intelligence is a capability, not a product. 

“You don’t focus on ‘I’m going to go do AI,’ says Peter Guerra, North America chief data scientist at Accenture. “You focus on ‘I’m going to do supply chain better, and I’m going to leverage AI to do that.’” Peter Guerra, North America chief data scientist at Accenture.

Hughes Network Systems Adds AIOps to its Managed Services

We are, by most estimates, relatively early in the artificial intelligence life cycle. Most AI-based capabilities tracked by Gartner are five to 10 years away from widespread commercial use. Some might say the same is true of connectivity service provider use of AI to support their own network operations.

But it is coming. Hughes Network Systems, for example, has commercial availability of its artificial intelligence capability for supporting information technology operations (AIOps).

Integrated into the company’s HughesON Managed Network Services, the Hughes AIOps feature is already in use across more than 32,000 managed sites. The technology automatically predicts and preempts—or “self-heals”—undesirable network behavior, preventing service-disrupting symptoms in 70 percent  of cases, HNS says.

Hughes says it s the first managed services provider to deliver a self-healing WAN edge capability to enterprise customers.

source: Gartner 

Of course, AI will have impact in many other ways. At two recent sessions of the PTC Academy, a training course for mid-career telecom professionals, the point was made that artificial intelligence, more automated business processes and competitive pressures on profit margins all would combine to reduce headcount in the industry. That is not a judgment about the morality of the trend, just a prediction of what will happen. 

Nor are such observations in any way denying that new jobs that will almost inevitably be created as the automation, artificial intelligence and “substitute machines for humans” trends unfold. 

Big technology changes have happened before. Much-higher mechanization of agriculture drove most U.S. residents off farms and into urban centers, where those people and their descendants worked in new roles. A shift of value from goods to services likewise has shifted people out of factories and created new jobs elsewhere in the economy, particularly in a wide range of services roles. 

While the shift within the connectivity industry might not be that pronounced, industry headcount has been dipping for some decades, though offset by growth in the mobility segment. In the U.S. market, you can see the slow attrition of fixed network employment since the internet bubble peak and crash. 

The emergence of the mobility business as the industry growth driver was accompanied by job expansion in that segment of the business, stabilizing around 2002 and then falling after 2009. 

source: Bureau of Labor Statistics

To the extent that profit margins continue to be under pressure, and industry revenue growth anemic (less than one percent per year, globally), we should expect more substitution of machine operations for humans.