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.
Übungsprojekt
Learners will create hands-on projects designed to mirror real-world LLM application workflows. They will begin by working with pretrained transformer models on Hugging Face, analyzing tokenization mechanisms, exploring embeddings, and building efficient NLP pipelines for tasks such as text classification and sentiment analysis.
They will then progress to developing a retrieval-augmented knowledge assistant using LangChain, FAISS, and a variety of document loaders, integrating capabilities such as memory, tool use, and external API interactions.
In the final course, learners will containerize and deploy a fully functional LLM system using FastAPI, Docker, and cloud platforms, incorporating monitoring, evaluation, and CI/CD practices to ensure production readiness.
By the end of the program, learners will have built a robust portfolio of projects that will demonstrate their ability to architect, integrate, and deploy LLM-driven systems in practical, real-world environments.
