Speeding Up Media Production With AI Processing At The Edge

The use of artificial intelligence technology is booming in the media and entertainment industry. Statista forecasts that the worldwide AI software market will grow to $126 billion by 2025. With AI, broadcasters can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages to improve their operational efficiencies, optimize workflow and speed up productivity to monetize digital assets.

By taking AI a step further and performing critical pre-processing tasks at the edge of their workflows, broadcasters can achieve lower and more predictable costs with significantly reduced complexity.

Why AI Solutions Are Thriving

One of the most significant challenges that broadcasters face today is finding and accessing content quickly. Editors typically spend an average of one in 10 hours searching for content due to a lack of metadata tagging and general organization.

Editors need the power to search and retrieve content instantly, anywhere in the world. AI/machine learning storage solutions can help achieve this by simplifying metadata enrichment to improve content indexing, including video clips and details about those clips. As routine or manual tasks are automated, productivity improves and content creators and editors have a lot more time for creativity, which can spur innovations in new productions or services and result in revenue growth.

AI’s Evolution From Cloud To The Edge

Traditionally, AI processing services have focused on providing media companies with a way to use AI-embedded products in the cloud.  While cloud-enabled apps, services and tools have become invaluable in media production for their ability to help companies meet deadlines and reduce operational costs, the cloud has unpredictable costs. The time and effort required to upload and download in the public cloud, not to mention the egress fees, has made the cloud more expensive than previously anticipated, offsetting its many benefits through unnecessary complexity.

When broadcasters split processes between the cloud and the edge (i.e., a remote location), they can save a substantial amount of money and time during content production. Some of the key applications for AI/ML storage solutions in the media and entertainment industry include object and facial recognition, audio and video transcription, language translation, automated video editing, content recommendations, auto captioning and more.

A leading sports organization recently deployed AI/ML storage to improve efficiencies and expand its reach. Prior to using AI, the organization was only delivering content to about 20 countries. By automating many of its processes, including language translation, transcriptions and content distribution via AI, the organization successfully extended its video services to 120 countries using the same sized team. This is a great example of how content providers can reach broader audiences with AI/ML-based storage solutions.

Unlocking The Power Of AI/ML

No longer just a buzz word, AI is being put to practical use in the real world and helping broadcasters unlock the value of historic content libraries. A handful of innovative companies have developed groundbreaking new application-centric services for content production workflows in the media and entertainment space that are now performing critical preprocessing tasks at the edge of workflows to improve efficiency, reduce costs, speed up time of delivery and monetize digital assets faster. While significant advances have been made in a short number of years, we are just beginning to tap the power of AI in broadcast.

Jason Perr is AI project consultant at Perifery (a division of DataCore).