TAG Network

The OpenAPI specifications for Kubernetes provides taxonomy, but augmenting a graph data model with formalized ontologies enables any number of capabilities, one of the more straightforward is the inferencing requisite for natural language processing, and consequently, a human-centric query / response interaction becomes becomes possible. More importantly, more advanced systems can be built when a graph data model of connected systems is upgraded to be a knowledge semantic graph. Expected Outcome: - YAML-formatted definition of one or more relationships per Kubernetes resource. - Documentation of each relationship - per component. - Development of new types of genealogies - new types of ways in which resources relate to one another and how these relationships might be visualized. - Verification of functional relationships

On a periodic basis, the CNCF would like to present a public challenge to those that are interested in participating (e.g. “Challenge: Distributed Tracing with Jaeger”). Your mission in this internship is technical content creation of said challenges through use of markdown, Meshery, and any number of other CNCF projects. Challenges will be created using the Meshery Playground and potentially published in the proposed CNCF Hub. They will be similar too, but slightly different from these [example tutorials](https://docs.meshery.io/guides/tutorials/). Understand that your challenges will be promoted through CNCF channels, reviewed by various project maintainers, and that each challenger (participant) will receive a certain number of points, depending upon whether or not they successfully complete the challenges that you create and in what timeframe they complete those challenges (the faster, the more points). Your challenges will need to vary in level of difficulty. Expected Outcomes: - 10+ new challenges published in CNCF Hub (and Meshery Catalog and Artifact Hub). - Challenges can contain more than one objective. Points are earned for each objective completed. - Bonus: Extend one or more of Meshery’s Learning Paths.

While the OpenAPI specifications for Kubernetes offer a taxonomy, integrating a graph data model with formalized ontologies unlocks a multitude of capabilities. Among these, enabling inferencing necessary for natural language processing stands out as a straightforward application. This, in turn, facilitates the possibility of a human-centric query/response interaction. Importantly, advancing to a knowledge semantic graph from a connected systems' graph data model opens the door to building more sophisticated systems. Expected Outcome: - Identifying new technologies from CNCF landscape and creating new YAML-formatted definitions of one or more relationships.

On a periodic basis, the CNCF would like to present a public challenge to those that are interested in participating (e.g. “Challenge: Distributed Tracing with Jaeger”). Your mission in this internship is technical content creation of said challenges through use of markdown, Meshery, and any number of other CNCF projects. Challenges will be created using the Meshery Playground and published (in what is potentially the CNCF Hub). They will be similar too, but slightly different from these [example tutorials](https://docs.meshery.io/guides/tutorials/). Understand that your challenges will be promoted through CNCF channels, reviewed by various project maintainers, and that each challenger (participant) will receive a certain number of points, depending upon whether or not they successfully complete the challenges that you create and in what timeframe they complete those challenges (the faster, the more points). Your challenges will need to vary in level of difficulty. Expected Outcome: - 5+ new challenges published - Challenges can contain more than one objective. Points are earned for each objective completed. - Bonus: Extend one or more of Meshery’s Learning Paths.

The OpenAPI specifications for Kubernetes provides taxonomy, but augmenting a graph data model with formalized ontologies enables any number of capabilities, one of the more straightforward is the inferencing requisite for natural language processing, and consequently, a human-centric query / response interaction becomes becomes possible. More importantly, more advanced systems can be built when a graph data model of connected systems is upgraded to be a knowledge semantic graph. Expected Outcome: - YAML-formatted definition of one or more relationships per Kubernetes resource. - Documentation of each relationship - per component. - Development of new types of genealogies - new types of ways in which resources relate to one another and how these relationships might be visualized. - Verification of functional relationships