Course Overview
This intensive one-week course is for developers and ML engineers looking to leverage Large Language Models (LLMs) within an enterprise context. Focuses on practical implementation using Python, covering Retrieval-Augmented Generation (RAG), fine-tuning techniques, vector databases, and securing LLMs for private company data.
Learning Objectives
Upon completion of this course, you will be able to:
- Understand the architecture and capabilities of modern LLMs.
- Implement RAG pipelines for efficient information retrieval from custom documents.
- Utilize vector databases (e.g., ChromaDB, Pinecone) for semantic search.
- Perform basic fine-tuning of open-source LLMs.
- Secure LLM deployments for data privacy and compliance.
- Build AI-powered applications using Python frameworks (LangChain).
- Evaluate LLM performance and choose appropriate models.
Key Topics Covered
Module 1: LLMs Fundamentals
- Introduction to Large Language Models (GPT, Llama, etc.)
- Prompt Engineering Techniques
- Understanding Embeddings and Vector Representations
Module 2: Retrieval-Augmented Generation (RAG)
- The RAG Architecture Explained
- Building Data Pipelines for Document Ingestion
- Vector Databases: Concepts and Usage (ChromaDB)
- Implementing Semantic Search with RAG
Module 3: Fine-Tuning & Customization
- When and why to fine-tune an LLM
- Introduction to LoRA and other efficient tuning methods
- Practical Fine-Tuning using Hugging Face Transformers
- Evaluating Fine-Tuned Models
Module 4: Building AI Applications with Python
- Introduction to LangChain framework
- Creating LLM Agents and Chains
- Integrating LLMs into existing Python applications
- Building a simple RAG-based Chatbot
Module 5: Enterprise Deployment & Security
- Strategies for deploying LLMs securely
- Data privacy considerations for enterprise data
- Introduction to Private LLM deployments
- MLOps for LLMs: Monitoring and Management
Target Audience
- Software Engineers & Developers
- Machine Learning Engineers
- Data Scientists
- AI Researchers
Prerequisites
- Strong proficiency in Python programming.
- Familiarity with Machine Learning concepts.
- Basic understanding of APIs and web services.
- Experience with ML libraries like TensorFlow or PyTorch is a plus.