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LLM Engineering & Deployment

LLM Engineering & Deployment

Duration: 40 Hours

1. Introduction to LLM Engineering

Understanding Large Language Models (LLMs)
Evolution of AI Models: From Early NLP to LLMs
Comparing GPT, Claude, Gemini, LLAMA, and Open-Source Models
LLM Architecture: Transformers, Attention Mechanisms, and Tokenization
Understanding LLM Parameters: Context Windows, Token Limits, and Scaling Laws

2. Multimodal LLMs: Expanding AI Capabilities

What are Multimodal LLMs?
Integrating Text, Image, and Audio in LLMs
Hands-on: Implementing Multimodal AI using OpenAI and DALL·E
Building a Multimodal AI Assistant with Audio & Image Processing
Real-world Use Cases of Multimodal AI

3. LLM Training: From Data to Model Optimization

Understanding Pretraining, Fine-Tuning, and Transfer Learning
Finding and Preparing Datasets for LLM Training
Data Curation Techniques for High-Quality Training
Evaluating Model Performance: Loss Functions & Business-Centric Metrics
Hyperparameter Tuning: LoRA, QLoRA, and Optimized Training
Quantization Techniques: Reducing Model Size for Efficient Training

4. Deploying LLMs: Scaling for Production

LLM Deployment Pipeline: From Business Use Case to Production
Cloud vs. Local Deployment: Choosing the Right Infrastructure
Setting Up Ollama for Local LLM Deployment
Serverless AI Deployment: Running LLMs Efficiently in the Cloud
Fine-Tuning vs. Prompt Engineering vs. RAG: When to Use What?
Building Real-Time Streaming LLM Applications

5. Multi-Agent AI Systems: Autonomous AI Workflows

Introduction to Multi-Agent Systems in AI
Agentic AI: Planning, Autonomy, and Memory for AI Agents
Building AI Agents with LangChain, OpenAI, and Gradio
Designing an Agentic AI System for Automated Workflows
Enhancing AI Agents with Structured Outputs & API Integrations
Case Study: Implementing a Multi-Agent AI Chatbot

6. Retrieval-Augmented Generation (RAG) for LLMs

RAG Fundamentals: Combining External Data with LLMs
Implementing Vector Embeddings for Efficient Information Retrieval
Building a DIY RAG Pipeline: OpenAI Embeddings & ChromaDB
Optimizing RAG Systems for Faster and More Relevant Responses
Switching Vector Stores: FAISS vs. Chroma for RAG Pipelines
Debugging RAG Systems: Troubleshooting and Fixing Common Issues

7. Evaluating & Optimizing LLM Performance

Evaluating LLMs: Business vs. Model-Centric Metrics
Benchmarking LLMs: GPT-4 vs. Claude vs. LLAMA 3
Human-Rated Language Models: Understanding LM Sys Chatbot Arena
Measuring Model Efficiency: Speed, Cost, and Response Quality
Fine-Tuning Performance Analysis: Weights & Biases Tracking
Post-Deployment Monitoring: Keeping LLMs Efficient Over Time

8. Final Project: Building & Deploying an LLM-Based Solution

Hands-on Implementation: Choose Between Chatbot, Multi-Agent System, or RAG Application
Model Selection & Data Preparation
Training, Fine-Tuning, or Retrieval-Augmented Optimization
Deployment on Local Machine (Ollama) or Cloud (AWS/Azure)
Performance Benchmarking & Optimization
Final Presentation & Discussion

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