# Service: Managed Edge Operations (MLOps)

## metadata
- title: Managed Edge MLOps Service | Klyff
- route: /services/managed-operations
- service_level: 24x7 enterprise monitoring
- uptime: 99%+ SLA guarantee

## Overview
Turnkey edge ML infrastructure management for industrial teams that want hands-off operations. Klyff provides round-the-clock device monitoring, automated model drift detection, staged over-the-air updates, and rapid engineering escalation.

## Performance Benchmarks
- **Operational Model:** Fully managed edge ML device infrastructure.
- **Model Lifecycle:** Completely hands-off retraining, deployment, and testing.
- **System Uptime:** 99%+ SLA-backed fleet availability.
- **Continuous Improvement:** Ongoing monthly optimization and accuracy audits.

## What We Do

### 1. End-to-End Platform & Model Deployment
- Containerizes, compiles, and provisions optimized AI models across your target edge hardware fleet (Jetson, Coral, Intel, x86 IPCs).
- Automates runtime configuration, dependency isolation, and hardware-accelerated drivers.
- Ensures identical, auditable execution across all plant floor devices.

### 2. Real-Time Fleet Monitoring (100+ Devices)
- Complete live telemetry for every deployed node: CPU/GPU load, memory headroom, inference latency, camera FPS, and operating temperatures.
- Automated anomaly detection flags hung pipelines, thermal throttling, or dropped sensor connections.
- Centralized historical telemetry logs for long-term capacity planning.

### 3. Version Control & Staged OTA Deployments
- Rigorous versioning for every model weights update and firmware container.
- Staged canary deployments: new models run on test lines first, metrics are verified, and only then rolled out across the fleet.
- Instant single-click rollback prevents production downtime if anomalies arise.

### 4. Autonomous Model Drift Detection & Retraining Triggers
- Tracks live inference confidence distributions to detect drift caused by camera re-alignment, lighting shifts, or tooling wear.
- Automatically initiates retraining pipelines when accuracy dips below defined quality thresholds.
- Hands-off data curation, training, validation, and staged rollout according to defined enterprise policies.

### 5. Stakeholder Performance Dashboards
- Tailored views: Quality managers monitor line defect rates, Maintenance teams view machine stress indexes, and Plant Engineers observe latency and memory usage.
- Interactive Statistical Process Control (SPC) charts with drill-down to specific camera frames or sensor logs.

### 6. Monthly Engineering Optimization Reviews
- Monthly deep-dive reviews by Klyff ML engineers to analyze latency trends, edge hardware efficiency, and emerging defect categories.
- Actionable recommendations with projected ROI impact for ongoing performance gains.

### 7. 24x7 Priority Support & Incident Escalation
- Around-the-clock monitoring hotline and rapid response for production line outages.
- Direct escalation to Klyff embedded systems and vision engineers for root-cause diagnosis.

## Best Suited For
- Manufacturing teams lacking internal ML ops and embedded engineering bandwidth.
- Multi-site enterprises scaling across dozens of plants.
- Mission-critical assembly lines with zero downtime tolerance.