[Tracker]: Near Edge MVP

[LaVLaS]

Description

This MVP delivery is focused on delivering a feature set into ODH that supports deploying models to near edge locations. This is a top level tracker to provide an single issue entry point for external visibility

This feature set will include but is not limited to:

• Define the standard terms that will be used to through this AI Edge initiative to ensure that

• [Tracker]: Near Edge MVP - MLOps Pipelines #156

  • Fetch a trained model from a repository: Git, S3, Model Registry
  • Build a model into packaged into an immutable container with all of it's required dependencies
  • Test the model in a staging environment to verify that it is working as intended
  • Deploy the packaged model into a remote repository that will be made available at the Near Edge

• [Tracker]: Edge Deployments odh-dashboard#1812 (UX/UI) - Provide a unified user experience for the user to minimize the requirement to understand all of the backend services required to build, deploy and monitor models running in near edge environments

• [Tracker]: Near Edge MVP - Model Management in Near Edge Environments #157

  • Integration with ACM and ArgoCD to support deploying immutable model containers into near edge environments
  • Utilize ArgoCD to support the ACM Pull model for disconnected near edge environments
  • Utilize a GitOps to provide a source of truth for model deployment configuration

• [Tracker]: Near Edge MVP - Observability #158

  • Monitor the operational metrics of my containerized model at the near edge.

• [Tracker]: Near Edge MVP - CI/CD #159

  • Support for test a immutable monitor container
  • Updates to Pipelines required for the MLOps workflow
  • Integration is other dependency that are required for delivering immutable model containers to near edge locations:

[LaVLaS]

Closing this as the work will be tracked in RHOAIENG-218