Video Training Data: AI's Next Frontier and Biggest Challenge
Content aggregators and data brokers have tapped into a new revenue stream in curating and selling various media assets for AI/ML Data Training.
But when it comes to video assets, which are the most sought-after AI/ML training assets, managing and monetizing unstructured video collections poses a big challenge.
Underutilized Archives
Vast untagged and unstructured video and media libraries remain unmonetized because brokers do not know what's in their libraries and buyers cannot locate desired assets.
High Storage & Transfer Costs
Hosting large-scale media assets on the cloud can lead to costly egress fees.
Complex Content Search & Discovery
Manual searches often miss potential revenue streams, and AI buyers can't find what they need quickly—and brokers experience difficulties in creating buyer-facing workflows.
Time-Intensive Metadata Tagging
Generating descriptive metadata and tags for diverse assets strains resources and slows speed to market.
Data Processing
Video files need to be processed and prepared to be valuable as training data: de-duped, cleansed for NSFW, shots segmented etc.
Time Management
Optimize your schedule with intelligent time tracking and task prioritization.