Mastering Large Language Models: Hands-On Development, Engineering, and Production Strategies - Tapa blanda

Sinopsis

Turn AI theory into practical, production-ready skills.

Large Language Models (LLMs) are redefining how businesses operate—powering chatbots, intelligent assistants, and analytics pipelines across industries. Yet most resources stop at theory. This book bridges the gap between concept and implementation, guiding you through the entire lifecycle of an LLM: training, fine-tuning, evaluation, deployment, and scaling.

Whether you’re an engineer, data scientist, or AI enthusiast, you’ll learn exactly how to build, optimize, and deploy real LLM-based systems using Python, Hugging Face, and modern frameworks.

Inside You’ll Learn

• Core LLM Foundations – Tokenization, embeddings, and Transformer architectures explained with working code.
• Fine-Tuning Strategies (Full, LoRA, Prompt Tuning) – Adapt models for specialized business tasks on limited hardware.
• Serving & Deployment – Turn your trained model into a FastAPI-powered API ready for production.
• Optimization & Evaluation – Boost performance using quantization, batching, and modern metrics (BLEU, ROUGE, Accuracy).
• Debugging & Governance – Handle hallucinations, drift, and ethical risks in real-world environments.
• Industry Use Cases – Finance, Marketing, HR, and IT workflows with full demos and case studies.
• Appendices with End-to-End Projects – Build your own customer service chatbot and multi-modal AI pipeline from scratch.

• Code-first approach: Every concept is paired with runnable examples.

• Real-world case studies: Learn from finance, healthcare, and enterprise deployments.

• Practical, not theoretical: Build deployable models using accessible tools and cloud environments.

• Future-proof: Stay ahead of trends like parameter-efficient fine-tuning and AI governance.

• AI Engineers & Data Scientists building production LLMs

• Developers integrating GPT-style APIs into products

• Tech leads seeking enterprise-ready AI solutions

• Students and researchers transitioning from NLP to applied LLMs

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