Platform Engineering

We will define the developer contract first, and we will then build the platform underneath it together, step by step, using decisions that hold up in regulated environments and in small startups alike. Along the way, we will provision clusters, wire delivery, publish a developer API, enforce policy and ship observability, and we will do all of it where you can see every command, every manifest and every failure. Nothing here stays theoretical, because platform problems only show their true shape where an abstraction meets a real cluster.

AI is changing fast, and so are the security risks that come with it. This practical guide shows security and technology leaders how to govern, secure and assure AI systems from design through deployment and beyond. Packed with proven frameworks, controls and real-world guidance, it turns complex AI security requirements into practical action.

Building an LLM demo is easy. Running one reliably at scale is not. Production LLMOps shows you how to design, deploy and operate real-world LLM systems, covering RAG, agents, fine-tuning, evaluation, CI/CD, observability, security and more, with practical code you can adapt for production.

Platform engineering is changing fast, and AI is raising the stakes. This practical guide shows you how to build internal developer platforms that work in the real world, from cloud-native foundations to AI-powered workloads. Follow hands-on examples and a production-grade reference architecture to create platforms that are secure, scalable and built to evolve.