$1,200.00 Fixed
Data-Science Angle (Unique Structure)
You are Head of Data Infrastructure at a IoT analytics company. pgBackRest backups spike IOPS at fixed 6 h intervals—correlating with user query latency jumps. You want an AI-driven scheduler that predicts low-traffic windows and self-tunes backup start-time daily—no human calendar.
AI-Driven Outcomes
- Backup start-time moves daily ±90 min based on traffic forecast.
- Predictive R² ≥ 0.85 on CPU & IOPS time-series.
- Grafana anomaly alert if actual vs forecast >15 %.
ML-Ops Scope I Will Deliver
- Time-Series Data Pipeline
- Prometheus metrics : pg_stat_activity, node_cpu, node_disk_io_now.
- 3-month historical CSV export (1 min granularity).
- Prophet Forecast Model
- Python notebook (Prophet) → hourly predictions 7 days ahead.
- Hyper-parameter tuning : changepoint_prior_scale, seasonality_mode.
- Auto-Scheduler Micro-Service
- FastAPI service deployed on EKS → exposes /next-backup-window API.
- CronJob calls API daily 01:00 UTC → returns optimal start-time (±90 min).
- pgBackRest Integration
- Dynamic cron entry template (Jinja2) → start-time injected daily.
- pgBackRest config stanza with compression zstd + parallel 4
- Observability & Feedback Loop
- Grafana dashboard : forecast vs actual CPU (overlay).
- Alertmanager : anomaly >15 % → Slack #data-ops
- Model Retraining Pipeline
- GitHub Actions weekly → retrain model + push new container (Seldon).
AI-Ops Deliverables
- Prophet notebook (ipynb) + requirements.txt.
- FastAPI Dockerfile + Helm chart + Prometheus ServiceMonitor.
- Grafana dashboard JSON + Alertmanager YAML.
- CSV accuracy report : MAE, RMSE, R².
Why Only a Senior ML-Infra Engineer
- Prophet contributor + Prometheus maintainer experience.
- Carried 2 IoT unicorns through AI-driven scheduling; IOPS −42 %.
- 30-day model drift monitoring included (shared Slack).
- Italy
- Proposal: 0
- Verified
- Less than a week
Isabella Ferrari
Lazio , Italy
Member since
Oct 26, 2024
Total Job
8
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