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Field notes on cloud migration, enterprise AI, DevOps at scale, and zero trust, drawn from real delivery work. Each piece is built to be acted on, not just read.
Most enterprise AI never ships. The real reasons projects stall, from novelty-driven use cases to weak data and missing MLOps, and a practical framework to reach production with measurable ROI.
Lift-and-shift rarely pays off. A wave-based plan that ranks workloads by business risk and cloud readiness, so you cut run-cost and downtime while the business keeps moving.
Most AI programs stall in the pilot phase. A roadmap that sequences use cases by value and data readiness, names owners, and sets the guardrails that move models into production.
Platform engineering and golden paths that let dozens of teams ship safely. The DORA metrics worth tracking, and the ones that quietly mislead, so you measure real delivery performance.
A phased path to zero trust that starts with identity and segmentation, then earns its way to least-privilege access, strengthening posture without grinding delivery to a halt.
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Occasional, substantive thinking on enterprise technology. No noise.