Day 37 - March 18, 2026
Internship diary
- Role: AI Engineer - SynerSense
- Project: AnanaCare ML Control Plane (Phase 4: Promotion & Optimization)
- Hours: 8
Executive summary
- Implemented production-grade Model Promotion Workflow.
- Improved training log observability and metrics extraction.
- Hardened process management with
psutiland improved local/HF results sync.
Work summary
Backend: Model promotion
- Added endpoint:
POST /api/promote/{trial_id}. - Flow:
- Reads best hyperparameters from
trial_*.json. - Updates consolidated training config.
- Triggers final training run with prediction enabled.
- Reads best hyperparameters from
- Write path:
.anana-results/train/(structured, separate from tuning outputs).
Backend: Process management
- Integrated
psutilto manage process trees and cleanup terminated jobs. - Ensures child processes are terminated when a job is stopped from the UI.
Frontend: log monitoring and metric UX
- Added structured parsing layer to log viewer (beyond raw text).
- Extracted metrics: validation loss, epoch progression, learning rate.
- Real-time visualization via lightweight charts.
Sync improvements and consistency
- Refined local/Hugging Face result sync logic.
- Added manual sync to reconcile split-run environments.
- Ensures leaderboard reflects latest trials across local + cloud runs.
Impact: completed the loop from experiment run → monitoring → promotion into reproducible production model runs.
Technical stack & skills used
| Component | Technology | Applied skill |
|---|---|---|
| Backend | FastAPI | API design, workflow automation, process management |
| Process control | psutil | process tree handling, resource cleanup |
| Frontend | SvelteKit | real-time UI updates, log visualization |
| MLOps | Hugging Face | model tracking, remote sync, experiment management |
Learnings & outcomes
- Designed a model promotion pipeline from experimentation to reproducible production outputs.
- Validated the importance of process lifecycle management for long-running tasks.
- Converted streaming logs into actionable insights with structured parsing.
- Strengthened consistency across distributed environments (local + cloud).
Blockers & risks
- Potential race condition if multiple promotions run during sync; UI lock in place, backend-level lock advised.
- Log parsing depends on consistent output format; standardization needed for reliable metric extraction.
Next steps
- Dockerize the full backend + frontend stack for production readiness.
- Document the promotion workflow in user-facing docs.
- Add backend safeguards for concurrent promotion conflict prevention.