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PacktPublishing/LLM-Engineers-Handbook
The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
Book companion repo for 'LLM Engineer's Handbook' — walks you through building an LLM-powered 'digital twin' system end-to-end: data collection, fine-tuning Llama 3.1 with SFT+DPO, RAG retrieval, and AWS SageMaker deployment. Target audience is ML engineers who want a structured, opinionated path through the full LLMOps stack rather than piecing it together from blog posts.
DDD-style package layout (domain/application/infrastructure/model) is unusually disciplined for a tutorial project and teaches good habits alongside the ML concepts. ZenML as the pipeline orchestrator means reproducible runs with artifact tracking out of the box — not just scripts you run manually. The SFT→DPO training sequence is actually implemented end-to-end, not just described; the trained model is on HuggingFace so you can verify the results before committing to a $25 AWS run. CI/CD via GitHub Actions with credential leak checking (gitleaks) is a nice production-hygiene touch you rarely see in teaching repos.
The dependency surface is enormous — MongoDB, Qdrant, ZenML Cloud, AWS SageMaker, Comet/Opik, HuggingFace, OpenAI — and you need all of them working before the interesting parts run. One misconfigured service and you're debugging IAM roles instead of learning LLMs. Selenium/Chrome for crawling is fragile and platform-specific; the workaround instructions in the README (comment out the Selenium code) confirm this isn't a clean abstraction. Tests are basically empty stubs labeled 'examples' — there's no meaningful test coverage, so you can't tell if your setup is correct until you run an expensive pipeline. The whole system is tightly coupled to the authors' use case (a personal 'digital twin'), making it awkward to adapt to a different domain without significant restructuring.