Fall Tech

Zorroa Offers Precise Content Control Through Machine Learning to the Sports-Video-Production Community

The Sports Video Group is delighted to welcome Zorroa as a new corporate sponsor. The company’s machine learning (ML) integration platform breaks down the ML adoption barrier for media technologists seeking to automate metadata tagging and content management tasks at scale. It provides a no-code workflow for users without ML expertise to run their unstructured media assets against computer vision APIs from GCP, AWS, and Azure with just a few clicks.

The Zorroa platform was built ​by​ and ​for​ media technologists — engineers, product managers, and technology integrators who are being tasked to accelerate stages of the content management chain using AI/ML but do not have the budget, time, nor the data science expertise to implement the technology. As a result, they’re stuck having to manually facilitate media management tasks like image tagging, classification, and content moderation.

Unlike traditional data science or vendor integration cycles, Zorroa’s GUI-driven workflow enables agile ML experimentation without code, vendor lock-ins, or domain expertise, effectively compressing a 10- to 12-month development cycle down to an hour.

In a world where integrating machine learning into real business applications is reserved only for the most well-funded enterprise initiatives, Zorroa makes the advantages of ML accessible to teams of all sizes. While ML implementation — or even ML ​experimentation —​ is still nascent, expensive, and carries a low chance of ROI, the Zorroa platform’s no-code SaaS solution offers a way for innovation teams to apply ML to accelerate and modernize stages of the content management chain. Zorroa converts a process that is inherently unpredictable and long (as per course for data science) to one that is more akin to agile software development, helping teams manage expectations with their executive stakeholders.

Resulting benefits for media organizations are ML adoption at a lower price point, more rigorous experimentation to test, evaluate, and discover an optimal business case for ML, and faster time to market. Automated media asset tagging and classification using ML APIs drives not only operational efficiencies but opens up new revenue streams driven from better classified media archives as well as reduction of risk resulting from human errors introduced during manual content moderation.

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