To adequately address the market needs, Divitel will offer a plug-and-play solution to provide video delivery operators direct insights into their operations without a lengthy onboarding and configuration phase. We achieve this by sharing knowledge between deployments without exposing anything about the consumer. We use this knowledge to train machine learning models to detect potential incidents. There were three phases to our work:
1. Drawing from our extensive video domain knowledge, we explored different video delivery architectures. Here we identified components, connections between components, transport of information, and data sources for us to use in machine learning algorithms. We included entire end-to-end paths and the dependencies between parts to make precise predictions.
2. We designed a data architecture to support the data extraction, processing, storage, and loading of data for further use. We designed a pipeline that starts with unstructured log data and finishes with highly structured tabular data. We then use and analyze this data and provide initial insights, potential solutions through our engineers, or customer explanations. This data is also the basis for our next phase.
3. We developed machine learning models for fast and accurate incident detection that support us in providing excellent operational services. We use and deploy these models and instantly create insights on a customer deployment through our proprietary way of sharing knowledge. The models detect anything from slight variations that signal a potential impact that a human operator would overlook, to significant and directly impacting events.
Divitel will build this data platform based on the derived architecture following the steps mentioned above, thereby creating highly automated operational services. We will use and improve the machine learning models in this proof-of-concept and refine knowledge sharing between deployments for even better results. We are also in the process of applying for several patents derived from this project.
