Building LLMs with Hugging Face and LangChain
About this course
The Building LLMs with Hugging Face and LangChain Specialization teaches you how to create modern LLM applications from core concepts to real-world deployment. You will learn how LLMs work, how to build applications with LangChain, and how to optimize and deploy systems using industry tools. In Course 1, you’ll explore the foundations of LLMs, including tokenization, embeddings, transformer architecture, and attention. You’ll work with the Hugging Face Hub, Datasets, and Transformers pipelines, experiment with models like BERT, GPT, and T5, and build simple NLP workflows. In Course 2, you’ll build real LLM applications using LangChain and LCEL. You’ll create prompts, chains, memory, and RAG pipelines with FAISS, process documents, and integrate agents, tools, APIs, LangServe, LangSmith, and LangGraph. In Course 3, you’ll optimize and deploy LLM systems. You’ll improve latency and token usage, integrate structured and multimodal data, orchestrate workflows with LlamaIndex and LangGraph, build FastAPI services, add security, containerize with Docker, and deploy with monitoring and CI/CD. By the end, you’ll be able to create and deploy production-ready LLM applications using modern tools and MLOps practices.
75/100
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What you'll learn
- understand the core concepts of large language models (LLMs)
- build applications using LangChain and integrate various tools
- optimize and deploy LLM systems for production
Course objectives
- explore foundational concepts of LLMs including tokenization and attention mechanisms
- create and integrate NLP workflows using Hugging Face utilities
- build and deploy production-ready applications with security and monitoring in mind
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