Media Content Analytics
Media Content Analytics
A global Media company amassed massive video archives of movies, TV shows, Documentaries and other content and struggled with Search relevance and losing viewership that impacted their revenue.
The Media Content Analytics solution was developed using Amazon SageMaker and Deep Learning to organize and tag vast libraries of content. The solution uses deep learning models to automatically identify objects, scenes, and people in video frames, as well as transcribe speech and extract text from images. This allows for the automatic creation of rich, contextual metadata that can be used to quickly search and categorize content.
Benefits
- Improved Content Organization: By analyzing and tagging media content with metadata, the solution helps in organizing large libraries of media assets.
- Enhanced Search Ability: With accurate metadata tags, content becomes more searchable, enabling users to find specific media assets more quickly and easily.
- Better User Engagement: With media analytics, content creators and broadcasters can analyze viewer engagement and preferences to tailor content to specific audiences, leading to increased engagement and revenue.
- Increased Efficiency: Automated media analysis and tagging saves time and reduces errors associated with manual tagging, leading to increased operational efficiency.
- Cost-effective: AWS Media Analytics offers a cost-effective solution for media analysis, as users only pay for the specific services they use.
- Scalable: With AWS, users can scale media analytics solutions up or down according to their needs, ensuring that resources are allocated efficiently.
