Heads-up on AI: Get actionable insights & timely solutions when managing complex cross-workflow monitoring
By Shaun Lim
With broadcasters and media organisations looking for improved workflow efficiencies during a challenging business environment, the transition to software-defined infrastructures has continued to gain momentum.
A key strength of software-defined infrastructures, according to Andrew Broadstone, Senior Director of Product Management, Zixi, is the enabling of more agile workflows comprising on-premise and cloud assets.
“These are controlled by both broadcasters and their content and affiliate partners, and these workflows can be assembled quickly,” Broadstone told APB+. “Artificial intelligence (AI) and machine learning (ML), in turn, can enable broadcasters to understand the performance of these complex systems.
“This gives them a heads-up on problems during live playout and to see where instabilities are emerging, leading to higher operator confidence in more dynamic and complex configurations.
“AI and ML can also be used to automatically tune workflows based on network and content analysis.”
Broadstone also expects the adoption of AI and ML to continue accelerating, driven by broadcasters’ needs to do more with less, to manage increasingly complex network workflows with fewer people, and to lower costs.
He suggested, “AI/ML is most effectively deployed in the context of cross-workflow monitoring and configuration management where patterns can be observed across multiple systems. It also helps cut through the noise of meaningless alerts to show people where to focus so they waste less time.”
Having extensively studied the evolving role of AI and ML in live broadcast workflows, Zixi believes that while exact usage of these technologies will vary from organisation to organisation, the strength of these technologies lies in their ability to facilitate complex data analysis, generating both insights and solutions for users.
Alerting users to problems before they happen
To help broadcasters oversee, manage and deeply understand their inputs and outputs, Zixi is offering the Intelligent Data Platform (IDP), which uses advanced analytics, AI and ML to intelligently alert users to problems before they happen — through alerts, graphs, maps, charts, and data visualisations. This enables users to quickly interpret vast amounts of stream data and ensure broadcast-quality results.
Broadstone elaborated, “The Zixi IDP Insights feature visualises where irregularities and problems are emerging in complex workflows over time among hundreds or thousands of channels a broadcaster may be managing. It helps operators and engineers see which channels need attention, calling out outliers, allowing the user to drill-down to root causes quickly.”
Users can use the Zixi IDP Incidents feature to view details about signal degradation, and identify the correlated causes down to detailed network and content measurements. “It facilitates sharing relevant details to quickly cross organisational silos and solve problems that require multiple teams,” said Broadstone.
He also highlighted the Zixi IDP Health Score feature, which uses real-time machine learning to give operators a heads-up about problems up to two hours before they happen.
While broadcasters and media companies continue to look for ways to cut costs, save time, and improve quality, the move to hybrid workflows is also opening up new opportunities to be more dynamic, Broadstone pointed out.
“This includes adding content and distribution partners, quickly routing around network problems, and generally being more agile with fewer resources,” he said. “These workflows can also be more complex to manage and will increase the demand for analytics solutions that help operators see problems before they happen and respond quickly when they do.
“These imperatives will continue to drive the adoption of AI and ML solutions like the Zixi Intelligent Data Platform.”
The world of AI continues to grow
AI is going to be one of the largest industries of software that the world has ever seen, proclaimed Jensen Huang, Founder & CEO of Nvidia.
Huang was speaking as Nvidia announced record fiscal-year revenue of US$26.91 billion, driven partly by the company’s advances in AI. Excluding public clouds, between 20 million and 25 million global servers have Nvidia’s AI software installed, while Meta’s new AI Research SuperCluster system is also being built using Nvidia’s technology.
“I think we remain in the early days in our adoption [of AI], but it’s incredible how fast it has grown and how many different applications are now possible with AI,” Huang said. “It pretty much says that almost all future software will be written with AI or by AI.”
AI is also taking broadcast to new heights, said Sepi Motamedi, who leads global industry marketing for Professional Broadcast at Nvidia.
Writing in a blog post, she highlighted how AI-accelerated technologies are helping enhance the experience for content creation, distribution and consumption. “With AI, broadcasters can use advanced capabilities like recommender systems, gain deeper insights into content and viewership, and automate repetitive tasks to enhance productivity and efficiency,” Motamedi wrote.
Automatic decision on the best image to extract
For instance, Spain’s Mediapro Group created Automatic TV, which can process four 4K cameras at 60 frames per second in real time. Its AI algorithms automatically decide which is the best image to extract that shows the action on the field, thanks to Nvidia’s RTX GPUs and accelerated computing.
Nvidia is also supporting content creation from the cloud through collaborations with partners such as Cinnafilm and Ateme.
For the former, a global provider of video and audio optimisation solutions that uses the cloud to power creative workflows from anywhere, a large-scale, public cloud deployment of Nvidia GPUs allows Cinnafilm’s standards and resolutions conversions to be accessible by anyone with Internet connectivity.
NEA, Ateme’s full-IP solution for cloud digital video recorder and time-shifted content, is combined with the Nvidia Networking hardware acceleration solution to allow broadcasters to optimise their network usage and reduce storage requirements.
These examples, Motamedi suggested, are reflective of how traditional broadcast workflows are evolving, driven by the combination of a rising demand for entertainment content and the shift to streaming platforms.
She added, “To take advantage of the latest capabilities driven by AI and accelerated computing, broadcasters must transition to flexible, software-defined infrastructure.
“The new era of broadcast is here – and it’s powered by Nvidia.”
Question: Have you tried using advanced AI capabilities like recommender systems to gain deeper insights into content and viewership … or to automate repetitive tasks to enhance productivity and efficiency?
If so, please share your experience with APB+ readers by sending your comments to firstname.lastname@example.org.