CNCF TAG Network

Term 3 · Sep-Nov · 2 projects

• CNCF Challenges: Technical Content Creation

  LFX Mentorship · Listed in the official cohort

  @leecalcoteSource IssueLFX portal

  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. 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.

  • written English
  • Kubernetes
  • DevOps
  • and familiarity with any number of other CNCF projects
  • like Prometheus
  • CoreDNS
  • +13

• Interrelating Kubernetes Resources: Identifying relationships between all standard and custom resources

  LFX Mentorship · Listed in the official cohort

  @muzairs15Source IssueLFX portal

  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.

  • DevOps
  • Kubernetes Administration
  • Light familiarity with all of the CNCF projects and a desire to study each project and their operators/resources.

Term 2 · Jun-Aug · 2 projects

• Mapping CNCF Landscape one Relationship-at-a-time

  LFX Mentorship · Listed in the official cohort

  @leecalcoteSource IssueLFX portal

  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.

  • Familiarity with Helm charts and Artifact Hub
  • basic familiarity with Kubernetes
  • and familiarity with CNCF different projects would be helpful

• Technical Content Creation CNCF Challenges

  LFX Mentorship · Listed in the official cohort

  @leecalcoteSource IssueLFX portal

  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. 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.

  • written English
  • Kubernetes
  • DevOps
  • and familiarity with any number of other CNCF projects
  • like Prometheus
  • CoreDNS
  • +13

Term 1 · Mar-May · 1 project

• Mapping Kubernetes Resources: Identifying relationships between all standard and custom resources

  LFX Mentorship · Listed in the official cohort

  @muzairs15Source IssueLFX portal

  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.

  • DevOps
  • Kubernetes Administration
  • Light familiarity with all of the CNCF projects and a desire to study each project and their operators/resources.

Term 1 · Mar-May · 1 project

• Representing Kubernetes ontology in MeshModel

  LFX Mentorship · Listed in the official cohort

  @leecalcoteSource IssueLFX portal

  Network topologies and graph databases go hand-in-hand. 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. Deliverables (among other items): MeshModel capabilities browser Import/export of MeshModel models and components as OCI images augmentation of cuelang-based component generator

  • cuelang
  • Go
  • OCI

Term 3 · Sep-Nov · 1 project

• Kubernetes ontology and subgraph module design

  LFX Mentorship · Listed in the official cohort

  @leecalcoteSource IssueLFX portal

  Network topologies and graph databases go hand-in-hand. 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. Deliverables (among other items): a Kubernetes ontology using OWL as a popular (and mature) way of doing this. a cuelang-based component generator

  • cuelang
  • Go
  • neo4j