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Epic
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Resolution: Unresolved
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Undefined
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None
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None
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None
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Near Edge MVP
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False
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False
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To Do
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67% To Do, 0% In Progress, 33% Done
This feature set will include but is not limited to:
- Define the standard terms that will be used to through this AI Edge initiative
- Near Edge MVP - MLOps Pipelines
- 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
- 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
- Near Edge MVP - Model Management in Near Edge Environments
- 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
- Near Edge MVP - Observability
- Monitor the operational metrics of my containerized model at the near edge.
- Near Edge MVP - CI/CD
- Support for test a immutable monitor container
- Updates to Pipelines required for the MLOps workflow
The first deliverable for the MVP will be a Pipeline and OpenShift AI Dashboard page incubating as feature in the odh-dashboard repository
- links to
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