Leveraging AI/ML to Simplify Broadcast Operations

Leveraging AI/ML to Simplify Broadcast Operations

Andrew Broadstone, Director of Product Management for the Zixi Intelligent Data Platform sat down with AI Magazine to discuss the significant operational benefits that are enabled when trained machine learning algorithms and AI are fed streams of robust, instrumented streaming data.

Below is a transcript from the article.  The full AI Magazine article is available here.

 

One of the biggest challenges facing broadcast operations engineers is knowing when things are not working before the viewers’ experience is affected. In a perfect world, operators and engineers want to predict outages and identify potential issues ahead of time. Machine learning models can be orchestrated to recognise the normal ranges based on hundreds to thousands of measurements – beyond the ability of a human operator – and alert the operator in real-time when a stream anomaly occurs. While this process normally requires monitoring logs on dozens of machines and keeping track of the performance of network links between multiple locations and partners, using ML enables the system to identify patterns in large data sets and helps operators focus only on workflow anomalies – dramatically reducing workload.

Anomaly detection works by building a predictive model of what the next measurements related to a stream will be – for example, the round-trip time of packets on the network or the raw bitrate of the stream – then determining how different the expected value is from the next measurement. As a tool to sort through normal and abnormal streams, this can be essential, especially when managing hundreds or thousands of concurrent channels. One benefit of anomalous behaviour identification would be enabling an operator to switch to a backup link that uses a different network link before a failure occurs.

Anomaly detection can also be a vital component of reducing needless false alarms and reducing time wasted. Functionality such as customisable alerting preferences and aggregated health scores generated by threat-gauging data points assist operators to sift through and assimilate data trends so they can focus where they really need to. In addition, predictive and proactive alerting can be orders of magnitude less expensive and allow broadcasters to be able to identify the root causes of instability and failure faster and more easily.

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