New workload demands are turning data handling into a system-level design challenge rather than a back-end afterthought.

News cycles are increasingly dominated by debates about AI’s impact on power systems, consumer energy prices, grid emissions and grid resilience.

As I mentioned in the first article in this series, System Design For The AI Era: AI Data Centers Requires A Holistic Approach, data centers are the heart of the AI era. However, the exponential ...

Rather than positioning sustainability as a branding exercise, MBT’s approach reflects a practical engineering response to a ...

Why it matters: Operating data centers requires a lot of energy, and the enormous amount of water used to cool them is a growing environmental concern. As the expansion of AI data centers exacerbates ...

Microfactories are not just smaller replicas of mega-factories. They operate with radically different assumptions. Data is real-time and transient, not batch-processed. Production is modular, not ...

The rapid escalation of AI/ML workloads—driven by increasingly large language models—is reshaping high-performance computing and AI data center architectures. Real-time inference and large-scale ...

Modern control system design is increasingly embracing data-driven methodologies, which bypass the traditional necessity for precise process models by utilising experimental input–output data. This ...

Implementation uses role-based persona mapping to reconfigure interface views based on user intent and task context ...

Innovative CLIQ system removes lighting as a construction bottleneck by dramatically reducing labor time and operating ...