AI & Data Science for Operational Systems
Using data to improve decision-making in networks and infrastructure.
Applied Intelligence, Not Marketing AI
01 — Reality Check
Core Reality Check
AI does not replace engineers. It augments them — when applied correctly, with good data and clear objectives.
02 — Why AI
Why AI Makes Sense in Telecoms & Cloud
Telecom and cloud environments generate:
- Continuous telemetry
- Time-series data
- Event streams
- Logs
- Customer-impact signals
Unlike many industries, the data already exists. The challenge is turning it into something actionable.
03 — Data Sources
Data Sources & Engineering Context
Models without operational context are worse than useless
Network Telemetry
- SNMP metrics
- NetFlow / sFlow / IPFIX
- Interface counters
- Error rates
- Latency and packet loss
System & Cloud
- Logs (system, application, security)
- Metrics (CPU, memory, I/O)
- Cloud-native monitoring streams
- Event-driven data
Operational Data
- Incident tickets
- Change logs
- Maintenance windows
- Historical outage records
Business Signals
- Customer complaints
- SLA breaches
- Churn indicators
- Usage patterns
04 — Use Cases
Use Cases That Actually Work
Traffic Analysis & Forecasting
- Historical traffic modeling
- Seasonal and diurnal patterns
- Capacity planning support
- Growth trend detection
Anomaly Detection
- Identifying abnormal traffic patterns
- Detecting early signs of failure
- Reducing time to detection
- Separating noise from real issues
Performance Degradation Analysis
- Detecting slow-burn failures
- Correlating multi-layer symptoms
- Identifying precursors to outages
Fault Pattern Recognition
- Recurrent failure identification
- Root-cause clustering
- Vendor or configuration-related trends
Customer Experience Insights
- Mapping technical metrics to user impact
- Identifying churn risk signals
- Prioritizing fixes based on impact
Anomaly detection is one of the highest ROI applications of ML in operations.
05 — Modeling
Modeling Approach (No Black Boxes)
Techniques
- Time-series analysis
- Regression models
- Classification
- Clustering
- Change-point detection
Philosophy
- Start simple
- Validate continuously
- Prefer interpretable models
- Avoid unnecessary complexity
If a model cannot be explained to an engineer, it cannot be trusted.
06 — Limitations
Limitations & Honest Constraints
- Bad data produces bad models
- AI does not fix broken architecture
- Human oversight remains essential
Anyone promising otherwise is selling fiction.
07 — Integration
Integration Into Operations
Human-in-the-Loop
AI supports engineers, not replaces them. Alert prioritization and decision support.
Workflow Integration
Feeding insights into NOC dashboards, ticketing systems, and change management.
Continuous Learning
Models updated as systems evolve. Drift detection and ongoing validation.
AI systems must evolve alongside the networks they observe.
The gap in 2026 isn't models, it's plumbing: most operators still can't join flow telemetry, config state, and ticket history well enough to feed the agentic NOC tooling vendors are shipping — Azure SRE Agent and AWS DevOps Agent hit GA this spring, and roughly a quarter of Tier-1 carriers now run agentic AI in production for at least one domain, almost always assist-mode triage rather than closed-loop remediation. Classical methods still win where it counts: a well-maintained seasonal-decomposition forecast with drift alarms beats an unmonitored transformer on peering capacity planning, and the LLM's real job is compressing logs and drafting the RCA, not touching router config.
Explore Data-Driven Operational Intelligence
Start with understanding your data landscape and operational readiness.