CREDIT: VERTIV
Vertiv has integrated machine learning throughout its thermal management and power infrastructure portfolio, focusing on practical applications that deliver immediate operational value.
The company’ s ML-powered solutions target the intersection of efficiency and reliability, where incremental improvements significantly impact operational costs and uptime guarantees.
Vertiv’ s thermal management systems employ ML algorithms that learn facility-specific characteristics, optimising cooling performance for each unique environment.
Unlike generic control strategies, these models account for local weather patterns, building characteristics and workload profiles. The adaptive approach has proven particularly effective in facilities with variable IT loads, where static cooling strategies waste energy during low-utilisation periods.
The company’ s Liebert iCOM control system incorporates predictive maintenance capabilities across cooling and power infrastructure. By analysing vibration signatures, temperature patterns and electrical characteristics, ML models identify degrading components before failure. This enables planned maintenance during scheduled windows rather than emergency repairs during production outages.
Vertiv’ s recent emphasis on liquid cooling solutions leverages ML for managing the complexity of hybrid air and liquid infrastructure. As high-density AI workloads demand liquid cooling while traditional computing remains air-cooled, ML algorithms optimise resource allocation across both systems. The company’ s modular approach allows operators to deploy ML capabilities incrementally, avoiding disruptive forklift upgrades.
Vertiv demonstrates how infrastructure manufacturers can embed intelligence directly into equipment, creating self-optimising systems that reduce operational complexity.
122 January 2026