EBU activities related to Data, AI, and Machine Learning

PSM are facing the need to raise the quantity and the quality of content they create to address linear and non-linear channels. To do so, they are adopting agile technologies to accelerate the production and the management of massive amounts of data. The three pillars of these evolutions are data management, computing architectures and machine learning. EBU groups are addressing these three pillars by providing:

  • Development of open source tools;
  • Development of good practices and standards;
  • Dissemination.


Metadata and AI

The umbrella group that oversees the work in the Metadata and AI domain, maximizing knowledge sharing and collaboration. This group meets monthly.

Media Cloud Microservice Architecture (MCMA)

MCMA is a serverless/micro-services strategy for media. It aims to help developers to move to "Function as a Service" architectures. This project provides open-source libraries. Standardization of MCMA has started with SMPTE.


The EBU and several Members have created and continue to develop a tool for benchmarking machine learning-based Speech-To-Text (STT) systems. This open-source tool can be integrated into production workflows.

Metadata Models

The main activity of this group is to support Members in the domain of metadata, which plays an essential role in media. To do so, the group develops specifications and promotes innovation, such as the use of semantic technologies.

AI and Automatic Metadata Extraction

Work addressed in this group includes metadata schemas, the capabilities and performance of automatic metadata extraction tools, and the development of machine learning algorithms and related tools.


Metadata Developer Network

This community organizes the annual MDN Workshop for developers to share knowledge, learn from their peers, obtain feedback and collaborate on metadata-related and AI projects. The topics cover EBU Data & AI activities, metadata, machine learning and data processing architectures.

Currently the following deliverables are planned (green indicates the deliverable has already been delivered). Note that deliverables are dependent on enough participation in the work and that the planning is subject to change. New deliverables are added regularly.



  • status_med_12px.png Launch control: a web-based application to monitor and deploy complex cloud infrastructure (Q1 2021) 
  • status_med_12px.png MAM based on MCMA (Q1 2021) 
  • status_med_12px.png Cloud agnostic engine to develop workflows (Q2 2021)
  • status_med_12px.png Open-source libraries on GitHub for AWS, Azure, GCP (Q4 2021)
  • status_med_12px.png Containers management and standardised workflow integration  (Q4 2021)
  • status_med_12px.png Standardisation at SMPTE (Q4 2021)
  • status_med_12px.png Sharing good practices, state of the art and catalogue of AME tools (Q4 2021)
  • status_med_12px.png Development of AI models for audience identification for written content (Q1 2021)



2018 and before