Playbooks
RSSIn-depth references and blueprints I've built for certifications, architecture, and core topics — the material I share with the engineers I work with.
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The Postgres schema that lets you re-extract a corpus with a new parser, compare both versions in SQL, promote atomically and roll back with one UPDATE — with every extracted value tracing back to a page and a bounding box.
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What changes when AI work is throughput rather than conversation — the parser is the swappable seam instead of the model, queues replace serving, and residency gets enforced by queue topology rather than application code.
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Hybrid AI platforms fail at the network boundary, not the model layer — outbound-only architecture, CIDR planning, choosing between Cilium Cluster Mesh, Istio and no mesh at all, and portable identity across the edge.
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What you actually depend on if an AI platform has to run on two clouds and in your own datacenter — the eight rules, what portability costs, and why the gateway comes first and Kubernetes second.
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A practical 2026 reference covering fine-tuning techniques, alignment methods, training frameworks, data curation, distributed training, evaluation harnesses, model optimization, and the open-weight ecosystem.
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A practitioner's blueprint covering every layer of modern AI systems — foundation model selection, RAG, fine-tuning, agentic patterns, LLMOps, safety, cloud platforms, and the trade-offs behind each decision.
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An exam-day reference for the CCAR-F certification covering all five domains, the six exam scenarios, anti-patterns, trade-offs, and scenario triggers.
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A last-minute review guide for the Google Cloud Professional Cloud Architect exam covering the format, all six domains, service trade-offs, scenario triggers, HA/DR, cost optimization, and the new 2026 AI focus.
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An end-to-end guide to CI/CD and production operations for AI systems, covering the MLOps pipeline, tooling, cloud infrastructure, deployment and release patterns, monitoring, drift, LLMOps, cost optimization, and governance.