Deep dives into enterprise AI, MLOps, DevOps, and modern infrastructure.
Small language models, NPUs, and on-device inference are rewriting the economics of production AI. A field report for infrastructure leaders planning 2026-2027 capacity.
Token metering, idle GPU capacity, and spot-instance churn – the places AI budgets silently bleed. A practical FinOps playbook for production ML on AWS, GCP, and Azure.
If your ML team is fighting for GPUs while your cluster utilisation sits at 40%, the scheduler is the problem. A practitioner's guide to the Kubernetes controls that actually move the number.
The 2026 AI/MLOps maturity model is not the 2022 one. LLMs, agents, evals, and GPU scheduling have rewritten what "good" looks like. A clear-eyed self-assessment framework.
GPU supply normalised, hyperscaler margins compressed, and the economics of owning vs renting compute quietly flipped for a meaningful share of enterprise workloads. The numbers that matter.
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