Simplifying Live Broadcasting with Artificial Intelligence and Machine Learning

Broadcast media operations are taking advantage of ever-evolving AI and ML technologies to perform sophisticated event correlation, data aggregation, deep learning, and virtually limitless applications.

FREMONT, CA: Artificial intelligence (AI) and machine learning (ML) are seen as foundations of the next generation of technological innovation in the broadcast media sector for various reasons. These include the ability to examine vast amounts of data, find abnormalities and trends, and notify users of potential problems before they arise without human interaction.

The advantage of AI and machine learning for broadcasters is best understood by identifying the challenges every broadcaster wishes to avoid and then focusing on the use cases and broadcast media components where these technologies will have the biggest impact.

Considering the example of a live athletic game that stops streaming suddenly or frames dropping without apparent reason, broadcasters wish to avoid obstacles of this kind. Viewers may discover quality difficulties and begin to file complaints. Customers may have missed an important live sporting event because technicians cannot repair the problem in real-time. Consequently, revenue declines, and executives seek explanations.

During these challenging periods, there is no time to waste—viewers can quickly transfer to alternative services, which immediately impacts advertising revenue and reputation. Such circumstances are every broadcaster's worst fear. What went incorrect? How can we restore this backup promptly while limiting future risk? Who or what is at fault?

MINIMIZING COMPLEXITY

Every day, the networked world sees video workflows interacting, tangling, and integrating into new ways, enhancing information exchange, agility, and connection while simultaneously generating increasingly complex diagnostic concerns and issues. As more on-premises and cloud resources become connected with equipment from multiple vendors, sources, and partner organizations distributing to new device types, a vast amount of log and telemetry data is generated in the context of broadcasting.

Therefore, broadcast engineers receive more data than they can successfully process. They frequently muffle frequent notifications and alarms because data overload makes it difficult to determine what is essential and what is not. This inevitably results in teams being overburdened and needing more insight.

Advanced analytics and machine learning can assist with these difficulties by making sense of massive amounts of data, enabling human operators to sift through unimportant noise and zero in on areas where problems are likely to emerge before errors are detected. Advanced analytics allow media firms to optimize broadcast workflows by leveraging sophisticated event correlation, data aggregation, deep learning, and nearly endless applications. This increases your knowledge base, allows you to innovate faster than the competition, and allows you to plan for the future—both by enhancing your knowledge and opening up the possibility to save money and time by focusing on the information that matters most to their users and organization.