The Evolving Role of AI and Machine Learning in Live Broadcast Workflows

Posted September 2, 2021

Understanding how AI and machine learning benefit broadcast-quality video streaming in a variety of ways

Reducing the time spent combing through endless data while spotting trends and anomalies, learning and improving over time, lowering costs and enhancing monitoring capabilities – these are just a few of the ways the AI and ML enhance video workflows significantly. Exact usage will vary from organization to organization, but the strength of these technologies lie in their ability to facilitate complex data analysis, generating both insights and solutions for users. And as the models improve they produce reduced time to resolution for those on the backend who rely on these remote monitoring and management tools. Overall it is an enhancement for everyone – from the stream creators to the engineers all the way to end users.

The variety of business intelligence and analysis tools organizations have access to is helpful, but they can easily result in data overload and major time drains for engineers, operators, and anyone tasked with making streaming workflows run efficiently. The reason being the massive amount of individual values (which have to be assimilated and interpreted by an individual) create more information than any person can effectively process. In addition, as more on-premise and cloud-based resources are connected with equipment from different vendors, sources, and partner organizations distributing to new device types, there is an enormous, ever-expanding amount of log and telemetry data produced. This is where AI and ML models can create lasting, impactful value across the delivery chain.

Zixi’s Intelligent Data Platform uses AI and ML to help you oversee, manage, and deeply understand your inputs and outputs – generating a holistic picture of your system to keep you alerted without increasing noise

Once AI and ML systems are put in place, they can start detecting patterns of normal behavior and deviations from that behavior – without human intervention. This allows for automated alerting for engineers and technicians who no longer have to scour data constantly to pick up on potential issues before they become serious problems. Instead, AI and ML models found in Zixi for example will pick up on such elements as “Low Health Score” where a stream or component is at risk (of losing packets, for example) – notifying whoever is monitoring the system/workflow ahead of time to look into that particular element and adjust as needed. These notifications can also be adjusted to fit the needs of the system by its users, avoiding over-alerting. Once ML models are in place, they can serve as the anomaly detection team and save valuable time and asset costs of constant data analysis by a user.

AI and ML have virtually unlimited potential to cut costs, save time, and improve quality

These tools help reduce the human hours and costs by providing a unified dashboard for organizations so they have constant insight into stream health and quality, while reducing complexity. How are complicated systems simplified? Advanced analytics provide media companies the unprecedented opportunity to leverage sophisticated event correlation, data aggregation, deep learning, and virtually limitless applications to improve broadcast workflows. The benefit is to be able to do more with less, innovate faster than the competition and prepare for the future by increasing your knowledge base and opening the potential for cost reduction and time savings, honing in on the crucial details behind the data that matters most to both their users and organization rather than getting lost in the noise.

Learn more about the Intelligent Data Platform, AI and ML.

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