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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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.