Building LLMs with Hugging Face and LangChain

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

• Understand core LLM concepts, transformers, tokenization, and Hugging Face tools
• Build LangChain applications with prompts, memory, RAG, agents, and tools
• Integrate external APIs, vector stores, observability, and multi-agent workflows
• Deploy, secure, and monitor LLM systems using FastAPI, Docker, and cloud platforms

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work