The ultimate technical blueprint for developers and AI engineers to strip away execution overhead, leverage hardware-accelerated NumPy/PyTorch operations, and scale Python code effortlessly
Building production-grade AI systems requires more than just calling an API or running a basic notebook—it demands mastery over hardware constraints, memory bandwidth limits, and distributed inference architectures.My new book, AI Systems Engineering: From Prototype to Production, is now officially available. It’s a code-first, rigorous engineering manual covering everything from vLLM runtime internals, PagedAttention, and FP8/INT4 quantization to HNSW vector search, hybrid RAG, and autonomous multi-agent swarm architectures.Stop wrestling with out-of-memory errors and sub-optimal latency. Master the infrastructure that powers modern AI at scale.
A system-first engineering guide to building, deploying, operating, and securing production-grade Kubernetes clusters, cloud-native platforms, and AI inference workloads.
Discover G#, a modern .NET language built for developers who want less friction and more clarity. Go from your first line of code to production-ready applications while exploring the simplicity of Go, the expressive feel of Kotlin and Swift and the power of the CLR. Practical examples and real-world guidance make every chapter count.