Complete Generative AI Course With Langchain and Huggingface Course Syllabus

This course provides a hands-on, project-driven journey into generative AI development using Langchain and Huggingface. Designed for developers with basic Python and machine learning knowledge, it covers core concepts, integration techniques, and deployment strategies across eight comprehensive modules. With approximately 6 hours of on-demand video content, learners will progress from foundational concepts to building and deploying real-world generative AI applications, culminating in a capstone project that demonstrates end-to-end proficiency.

Module 1: Introduction to Generative AI & Langchain Basics

Estimated time: 0.5 hours

• Understand generative AI fundamentals and use cases
• Set up the Langchain development environment
• Explore Langchain core abstractions: chains, agents, and prompts
• Build a simple generative text pipeline

Module 2: Langchain Architecture & Design Patterns

Estimated time: 0.75 hours

• Dive into modular design patterns in Langchain
• Implement toolkit chains for specialized tasks
• Apply map-reduce and sequential chaining patterns
• Design maintainable and scalable AI workflows

Module 3: Huggingface Integration & Pre-trained Models

Estimated time: 1 hour

• Load and serve Huggingface models in Langchain pipelines
• Use tokenizers, transformers, and pipelines for NLP
• Integrate pre-trained models for text generation and classification
• Connect Huggingface APIs with Langchain components

Module 4: Fine-tuning Huggingface Models

Estimated time: 0.75 hours

• Customize pre-trained models using your own datasets
• Utilize Huggingface's Trainer and optimum APIs
• Evaluate model performance and detect overfitting

Module 5: Building RAG Pipelines

Estimated time: 0.75 hours

• Integrate vector databases (e.g., FAISS, Pinecone) with Langchain
• Implement document retrieval and embedding pipelines
• Generate context-aware responses using Retrieval-Augmented Generation

Module 6: Deployment Strategies

Estimated time: 1 hour

• Deploy generative AI models to cloud platforms (AWS, Azure, GCP)
• Host models on-premise for data privacy and control
• Containerize applications using Docker
• Orchestrate deployments with Kubernetes

Module 7: Optimization & Monitoring

Estimated time: 0.5 hours

• Set up logging, health checks, and performance metrics
• Apply quantization and distillation to reduce latency
• Implement caching strategies for scalable inference

Module 8: Capstone Projects & Real-World Applications

Estimated time: 1 hour

• Build an end-to-end chatbot with Langchain and Huggingface
• Develop a content generator or data augmentation tool
• Review best practices from production deployments

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with Jupyter notebooks or IDEs

What You'll Be Able to Do After

• Create advanced generative AI applications using Langchain and Huggingface
• Design and implement Retrieval-Augmented Generation (RAG) pipelines
• Seamlessly integrate and fine-tune Huggingface models in production systems
• Deploy scalable generative AI models on cloud and on-premise environments
• Optimize and monitor AI systems for performance and reliability