Lilly Tech Systems
Advanced
Best Practices
Production deployment patterns, organizational change management, and strategies for scaling predictive maintenance across your network infrastructure.
Phased Deployment
- Pilot: Start with one device type or network segment (3–6 months)
- Validate: Compare predictions against actual outcomes, tune models
- Expand: Roll out to additional device types and network segments
- Automate: Add proactive remediation for validated prediction types
- Optimize: Continuously improve models with new data and feedback
Organizational Change Management
- Training: Educate operations teams on interpreting AI predictions and health scores
- Trust building: Run in advisory mode first; let operators validate predictions before automation
- Process integration: Embed predictive insights into existing workflows and ITSM tools
- Metrics sharing: Regularly communicate prevented incidents and ROI to build organizational buy-in
Common Pitfalls
| Pitfall | Impact | Mitigation |
|---|---|---|
| Poor data quality | Inaccurate predictions | Invest in telemetry infrastructure first |
| Over-automation | Unintended outages from bad actions | Start with alerts, graduate to automation |
| Alert fatigue | Operators ignore predictions | High-precision models, smart suppression |
| Model staleness | Degrading accuracy over time | Automated retraining and drift detection |
Scaling Considerations
- Model per device type: Different hardware requires different prediction models
- Feature store: Centralize feature computation to avoid redundant processing
- Model registry: Track all model versions, performance, and deployment status
- Data retention: Keep at least 2 years of telemetry for seasonal pattern analysis
Congratulations! You've completed the AI Predictive Network Maintenance course. You now have the knowledge to build prediction models, create health scoring systems, implement proactive remediation, and demonstrate ROI for predictive maintenance programs.