Feature Description
Implement a shared downloader capability that:
- Reads per-application model registry configuration (model name, source URL, target path, version metadata).
- Downloads required model artifacts into runtime storage used by downstream services.
- Skips already valid artifacts (idempotent behavior).
- Reports per-model success, skip, and failure outcomes.
- Fails fast with actionable logs for blocking issues.
- Integrates with setup-stage orchestration flows (compose profiles, helm jobs, or pre-deploy hooks).
- Supports release readiness checks and rollback procedures tied to model availability.
Business Value
- Eliminates manual model copy across teams and environments.
- Reduces deployment failures caused by missing or incorrect model files.
- Standardizes model onboarding for new workloads across the suite.
- Improves release reliability with one common gate for model readiness.
Scope
- Standalone containerized downloader service reusable by all suite applications.
- Config-driven model registry format per app and workload including URLs, paths, and versions.
- Idempotent download behavior that skips existing valid artifacts.
- Retry and error handling with clear model-level status output.
- Setup-stage orchestration integration via compose or helm profile and job.
- Release gate checks and rollback runbook tied to model availability.
Dependencies
- ML and artifact teams publishing model URLs and versioned artifacts.
- Environment access, proxy, and network policy support for artifact repositories.
Solution Description
Build a centralized, standalone model provisioning service that runs before application startup and prepares required AI model artifacts in a consistent, validated location.
The service will be configuration-driven, containerized, and reusable across all suite applications without app-specific code changes. It will support model-level status reporting, resilient download behavior, and deployment gate integration so releases are blocked when required models are unavailable or invalid.
Acceptance Criteria
Any suite application can provide a registry and use the same downloader image without code changes.
Service downloads configured artifacts to target storage and reports success, skip, and failure per model.
Service exits with non-zero code on blocking failures and provides actionable logs.
Setup orchestration can run the downloader before application startup.
Go and no-go release checklist includes verified model readiness evidence.
Rollback path is documented and tested for artifact and download failures.
Alternative Description
No response
Feature Description
Implement a shared downloader capability that:
Business Value
Scope
Dependencies
Solution Description
Build a centralized, standalone model provisioning service that runs before application startup and prepares required AI model artifacts in a consistent, validated location.
The service will be configuration-driven, containerized, and reusable across all suite applications without app-specific code changes. It will support model-level status reporting, resilient download behavior, and deployment gate integration so releases are blocked when required models are unavailable or invalid.
Acceptance Criteria
Any suite application can provide a registry and use the same downloader image without code changes.
Service downloads configured artifacts to target storage and reports success, skip, and failure per model.
Service exits with non-zero code on blocking failures and provides actionable logs.
Setup orchestration can run the downloader before application startup.
Go and no-go release checklist includes verified model readiness evidence.
Rollback path is documented and tested for artifact and download failures.
Alternative Description
No response