Why CloudSci AI exists
CloudSci AI is a weekly field notebook for practical AI engineering, cloud platforms, Kubernetes, and infrastructure as code.
CloudSci field notes
AI engineering,
under production constraints.
One practical article each week about building AI systems and cloud platforms that remain secure, observable, reproducible, and useful after the demo ends.
Latest writing
Notes from the workbench.
Architecture decisions, implementation details, failure modes, and lessons worth carrying into the next system.
Editorial pillars
Four systems, one production surface.
The blog follows the places where AI applications meet identity, infrastructure, reliability, and human decision-making.
AI Engineering
Evaluation, agents, retrieval, model operations, safety boundaries, and human review.
Cloud Platforms
Landing zones, identity, network boundaries, governance, FinOps, and resilient foundations.
Kubernetes & IaC
AKS, workload identity, Terraform modules, GitOps, policy, and repeatable delivery.
Production Practice
Observability, incident learning, architecture trade-offs, security, and operational readiness.
Written from experience
By Gowrisha C. Vishwa Kumar.
Senior Cloud Platform Engineer writing about the practical boundary between AI applications and the cloud platforms expected to run them safely.