The MDN Workshop is the annual meeting point for developers working on Metadata and Artificial Intelligence in broadcasting. It is open to the public.
The event is organized under the EBU Production Strategic Programme by Media Information Management and AI (MIM-AI) and the Metadata Developer Network (MDN), an active community for developers to share knowledge, learn from their peers, get feedback and collaborate on metadata-related projects.
The workshop is open to the public. The format will be similar to MDN 2020: a two-day webinar with in-depth presentations, demonstrations, discussions.
Do you have a project you would like to present?
To submit your contribution, please send us an email. The deadline for contribution submissions is 12 March 2021. Notification of acceptance/rejection: 9 April 2021
We invite the following types of contributions:
- Hands-on demonstrations
- Panel discussions
We welcome contributions exploring Meta Data, AI, Data Engineering and Architecture. We also encourage transversal presentation with business cases that encompass these fields. Your presentation can be the opportunity to expose the problems you are working on, to communicate on your project and to propose collaboration via the EBU channels. The main topics include the following theme without being exhaustive:
• EBUCore and CCDM implementations
• Vocabularies and taxonomies
• Data models, ontolologies
• Metadata quality
• Metadata visualization
Artificial Intelligence (AI):
• Automatic metadata extraction (AME) on audio, video and writings
• Content tagging
• Content enrichment
• Social media analysis
• Explainability of AI
• Benchmarking of AI services
• Agile workflows in production, archives and distribution, CI/CD, DevOps
• Microservice architectures
• Cloud adoption in the context of media workflows, MCMA
• Breaking / Integrating data silos strategies
• Architecture patterns applied to media workflows (event sourcing, event streaming, pub/sub, etc.)
• Technology adoption strategies (buy vs. make)
• ETL (Extract Transform and Load)
• Master Data Management
• Data Lakes
• Data Hubs