TECH & AI
EDGE INTELLIGENCE
Edge computing infrastructure presents unique ML opportunities and challenges.
Distributed edge locations generate vast telemetry data but may lack connectivity for centralised analysis.
Federated learning approaches enable ML models to train across edge deployments without centralising sensitive data, while edge inference allows real-time decision-making despite network latency.
As edge infrastructure proliferates, ML orchestration across distributed resources becomes critical for operational efficiency.
Real-time analysis of network traffic, access patterns and system logs enables faster incident response and reduced attack surface exposure.
Capacity planning, once an annual exercise involving spreadsheet projections, has transformed into continuous ML-driven forecasting. By analysing utilisation trends, customer growth patterns and seasonal variations, ML models provide increasingly accurate predictions of future infrastructure needs. This enables operators to optimise capital expenditure timing, avoid overprovisioning and ensure capacity availability for revenue-generating workloads.
116 January 2026