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Advanced Hugging Face Workflows: Scaling LLMs, RAG Pipelines, and Autonomous AI Agents
Advanced Hugging Face Scaling LLMs, RAG Pipelines, and Autonomous AI Agents
Confused by Hugging Face’s sprawling ecosystem? Wondering how to scale your models, build robust RAG pipelines, or design intelligent AI agents without drowning in fragmented docs and YouTube tutorials?
You don’t need a cluster of A100s or a PhD in transformer theory. You need a focused, practical guide and that’s exactly what this book delivers.
Advanced Hugging Face Workflows is your no-fluff, hands-on manual to building real-world AI systems using the Hugging Face ecosystem. Whether you’re scaling language models, retrieving knowledge dynamically with RAG, or coordinating multi-agent systems, this book shows you exactly how to do it with fully working, battle-tested code.
Inside, you’ll learn how
Fine-Tune and Optimize with Confidence
Train LLMs efficiently using PEFT techniques like LoRA and QLoRA, accelerate training with DeepSpeed and Hugging Face Accelerate, and apply quantization to cut your compute bills without compromising performance.
Build Enterprise-Ready RAG Systems
Go beyond simple pipelines and build retrieval-augmented generation systems that actually scale. Learn how to integrate vector databases like FAISS and ElasticSearch, manage indexes, and evaluate retrieval quality with practical metrics and tools.
Deploy Like a Pro
Deploy LLMs and RAG stacks using FastAPI, Hugging Face Inference Endpoints, SageMaker, or self-hosted Docker stacks. Learn to monitor, log, autoscale, and productionize your systems with performance and stability in mind.
Design Autonomous AI Agents
Use LangChain, CrewAI, or custom orchestrators to build multi-agent systems that retrieve information, invoke APIs, call tools, and solve complex tasks. Learn how to handle memory, error recovery, reasoning, and safety protocols.
Establish Solid Engineering Practices
Structure your projects with modular, production-ready scaffolding. Apply reproducible training workflows, version-controlled configs, and scalable logging setups to ensure you’re ready for team collaboration or deployment to clients.
Whether you're an ML engineer, researcher, startup founder, or freelance AI developer, this book gives you the end-to-end patterns, deployment strategies, and hard-won tricks that separate hobby projects from professional-grade AI systems.
No fluff. No vague theory. Just clean, tested code and the systems thinking to use it wisely.
Confused by Hugging Face’s sprawling ecosystem? Wondering how to scale your models, build robust RAG pipelines, or design intelligent AI agents without drowning in fragmented docs and YouTube tutorials?
You don’t need a cluster of A100s or a PhD in transformer theory. You need a focused, practical guide and that’s exactly what this book delivers.
Advanced Hugging Face Workflows is your no-fluff, hands-on manual to building real-world AI systems using the Hugging Face ecosystem. Whether you’re scaling language models, retrieving knowledge dynamically with RAG, or coordinating multi-agent systems, this book shows you exactly how to do it with fully working, battle-tested code.
Inside, you’ll learn how
Fine-Tune and Optimize with Confidence
Train LLMs efficiently using PEFT techniques like LoRA and QLoRA, accelerate training with DeepSpeed and Hugging Face Accelerate, and apply quantization to cut your compute bills without compromising performance.
Build Enterprise-Ready RAG Systems
Go beyond simple pipelines and build retrieval-augmented generation systems that actually scale. Learn how to integrate vector databases like FAISS and ElasticSearch, manage indexes, and evaluate retrieval quality with practical metrics and tools.
Deploy Like a Pro
Deploy LLMs and RAG stacks using FastAPI, Hugging Face Inference Endpoints, SageMaker, or self-hosted Docker stacks. Learn to monitor, log, autoscale, and productionize your systems with performance and stability in mind.
Design Autonomous AI Agents
Use LangChain, CrewAI, or custom orchestrators to build multi-agent systems that retrieve information, invoke APIs, call tools, and solve complex tasks. Learn how to handle memory, error recovery, reasoning, and safety protocols.
Establish Solid Engineering Practices
Structure your projects with modular, production-ready scaffolding. Apply reproducible training workflows, version-controlled configs, and scalable logging setups to ensure you’re ready for team collaboration or deployment to clients.
Whether you're an ML engineer, researcher, startup founder, or freelance AI developer, this book gives you the end-to-end patterns, deployment strategies, and hard-won tricks that separate hobby projects from professional-grade AI systems.
No fluff. No vague theory. Just clean, tested code and the systems thinking to use it wisely.
364 pages, Hardcover
Published August 4, 2025
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