- Objectifs pédagogiques
- Sections du cours
- Avis
Mastering Generative AI and LLMs: An 8-Week Hands-On Journey
Accelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.
What you’ll learn
• Build advanced Generative AI products using cutting-edge models and frameworks.
• Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.
• Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.
• Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.
• Create real-world AI applications, including:
• A multi-modal customer support assistant that interacts with text, sound, and images.
• An AI knowledge worker that can answer any question about a company based on its shared drive.
• An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.
• An ecommerce application that accurately predicts prices of unseen products.
• Transition from inference to training, fine-tuning both Frontier and Open-Source models.
• Deploy AI products to production with polished user interfaces and advanced capabilities.
• Level up your AI and LLM engineering skills to be at the forefront of the industry.
About the Instructor
I’m Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I’ve co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I’m passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.
Projects:
Project 1: AI-powered brochure generator that scrapes and navigates company websites intelligently.
Project 2: Multi-modal customer support agent for an airline with UI and function-calling.
Project 3: Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
Project 4: AI that converts Python code to optimized C++, boosting performance by 60,000x!
Project 5: AI knowledge-worker using RAG to become an expert on all company-related matters.
Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
Project 7: Capstone Part B – Fine-tuned open-source model to compete with Frontier in price prediction.
Project 8: Capstone Part C – Autonomous agent system collaborating with models to spot deals and notify you of special bargains.
Why This Course?
• Hands-On Learning: The best way to learn is by doing. You’ll engage in practical exercises, building real-world AI applications that deliver stunning results.
• Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.
• Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.
• No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.
Course Structure
Week 1: Foundations and First Projects
• Dive into the fundamentals of Transformers.
• Experiment with six leading Frontier Models.
• Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.
Week 2: Frontier APIs and Customer Service Chatbots
• Explore Frontier APIs and interact with three leading models.
• Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.
Week 3: Embracing Open-Source Models
• Discover the world of Open-Source models using HuggingFace.
• Tackle 10 common Gen AI use cases, from translation to image generation.
• Build a product to generate meeting minutes and action items from recordings.
Week 4: LLM Selection and Code Generation
• Understand the differences between LLMs and how to select the best one for your business tasks.
• Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.
Week 5: Retrieval-Augmented Generation (RAG)
• Master RAG to improve the accuracy of your solutions.
• Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.
• Build a full business solution similar to real products on the market today.
Week 6: Transitioning to Training
• Move from inference to training.
• Fine-tune a Frontier model to solve a real business problem.
• Build your own specialized model, marking a significant milestone in your AI journey.
Week 7: Advanced Training Techniques
• Dive into advanced training techniques like QLoRA fine-tuning.
• Train an open-source model to outperform Frontier models for specific tasks.
• Tackle challenging projects that push your skills to the next level.
Week 8: Deployment and Finalization
• Deploy your commercial product to production with a polished UI.
• Enhance capabilities using Agents.
• Deliver your first productionized, agentized, fine-tuned LLM model.
• Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career.
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Day 1 - Cold Open: Jumping Right into LLM Engineering -
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Day 1 - Setting Up Ollama for Local LLM Deployment on Windows and Mac
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Day 1 - Unleashing the Power of Local LLMs: Build Spanish Tutor Using Ollama
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Day 1 - LLM Engineering Roadmap: From Beginner to Master in 8 Weeks
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5
Day 1 - Building LLM Applications: Chatbots, RAG, and Agentic AI Projects
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Day 1 - From Wall Street to AI: Ed Donner's Path to Becoming an LLM Engineer
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Day 1 - Setting Up Your LLM Development Environment: Tools and Best Practices
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Day 1 - Mac Setup Guide: Jupyter Lab and Conda for LLM Projects
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Day 1 - Setting Up Anaconda for LLM Engineering: Windows Installation Guide
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Day 1 - Alternative Python Setup for LLM Projects: Virtualenv vs. Anaconda Guide
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Day 1- Setting Up OpenAI API for LLM Development: Keys, Pricing & Best Practices
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Day 1 - Creating a .env File for Storing API Keys Safely
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Day 1- Instant Gratification Project: Creating an AI-Powered Web Page Summarizer
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Day 1 - Implementing Text Summarization Using OpenAI's GPT-4 and Beautiful Soup
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Day 1 - Wrapping Up Day 1: Key Takeaways and Next Steps in LLM Engineering
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Day 2 - Mastering LLM Engineering: Key Skills and Tools for AI Development
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Day 2 - Understanding Frontier Models: GPT, Claude, and Open Source LLMs
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Day 2 - How to Use Ollama for Local LLM Inference: Python Tutorial with Jupyter
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Day 2 - Hands-On LLM Task: Comparing OpenAI and Ollama for Text Summarization
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Day 3 - Frontier AI Models: Comparing GPT-4, Claude, Gemini, and LLAMA
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Day 3 - Comparing Leading LLMs: Strengths and Business Applications
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Day 3 - Exploring GPT-4o vs O1 Preview: Key Differences in Performance
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Day 3 - Creativity and Coding: Leveraging GPT-4o’s Canvas Feature
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Day 3 - Claude 3.5’s Alignment and Artifact Creation: A Deep Dive
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Day 3 - AI Model Comparison: Gemini vs Cohere for Whimsical and Analytical Tasks
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Day 3 - Evaluating Meta AI and Perplexity: Nuances of Model Outputs
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Day 3 - LLM Leadership Challenge: Evaluating AI Models Through Creative Prompts
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Day 4 - Revealing the Leadership Winner: A Fun LLM Challenge
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Day 4 - Exploring the Journey of AI: From Early Models to Transformers
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Day 4 - Understanding LLM Parameters: From GPT-1 to Trillion-Weight Models
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Day 4 - GPT Tokenization Explained: How Large Language Models Process Text Input
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Day 4 - How Context Windows Impact AI Language Models: Token Limits Explained
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Day 4 - Navigating AI Model Costs: API Pricing vs. Chat Interface Subscriptions
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Day 4 - Comparing LLM Context Windows: GPT-4 vs Claude vs Gemini 1.5 Flash
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Day 4 - Wrapping Up Day 4: Key Takeaways and Practical Insights
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Day 5 - Building AI-Powered Marketing Brochures with OpenAI API and Python
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Day 5 - JupyterLab Tutorial: Web Scraping for AI-Powered Company Brochures
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Day 5 - Structured Outputs in LLMs: Optimizing JSON Responses for AI Projects
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Day 5 - Creating and Formatting Responses for Brochure Content
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Day 5 - Final Adjustments: Optimizing Markdown and Streaming in JupyterLab
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Day 5 - Mastering Multi-Shot Prompting: Enhancing LLM Reliability in AI Projects
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Day 5 - Assignment: Developing Your Customized LLM-Based Tutor
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Day 5 - Wrapping Up Week 1: Achievements and Next Steps
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Day 1 - Mastering Multiple AI APIs: OpenAI, Claude, and Gemini for LLM Engineers
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Day 1 - Streaming AI Responses: Implementing Real-Time LLM Output in Python
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Day 1 - How to Create Adversarial AI Conversations Using OpenAI and Claude APIs
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Day 1 - AI Tools: Exploring Transformers & Frontier LLMs for Developers
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Day 2 - Building AI UIs with Gradio: Quick Prototyping for LLM Engineers
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Day 2 - Gradio Tutorial: Create Interactive AI Interfaces for OpenAI GPT Models
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Day 2 - Implementing Streaming Responses with GPT and Claude in Gradio UI
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Day 2 - Building a Multi-Model AI Chat Interface with Gradio: GPT vs Claude
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Day 2 - Building Advanced AI UIs: From OpenAI API to Chat Interfaces with Gradio
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Day 3 - Building AI Chatbots: Mastering Gradio for Customer Support Assistants
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Day 3 - Build a Conversational AI Chatbot with OpenAI & Gradio: Step-by-Step
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Day 3 - Enhancing Chatbots with Multi-Shot Prompting and Context Enrichment
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Day 3 - Mastering AI Tools: Empowering LLMs to Run Code on Your Machine
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Day 4 - Using AI Tools with LLMs: Enhancing Large Language Model Capabilities
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Day 4 - Building an AI Airline Assistant: Implementing Tools with OpenAI GPT-4
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Day 4 - How to Equip LLMs with Custom Tools: OpenAI Function Calling Tutorial
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Day 4 - Mastering AI Tools: Building Advanced LLM-Powered Assistants with APIs
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Day 5 - Multimodal AI Assistants: Integrating Image and Sound Generation
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Day 5 - Multimodal AI: Integrating DALL-E 3 Image Generation in JupyterLab
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Day 5 - Build a Multimodal AI Agent: Integrating Audio & Image Tools
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Day 5 - How to Build a Multimodal AI Assistant: Integrating Tools and Agents
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Day 1 - Hugging Face Tutorial: Exploring Open-Source AI Models and Datasets
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Day 1 - Exploring HuggingFace Hub: Models, Datasets & Spaces for AI Developers
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Day 1 - Intro to Google Colab: Cloud Jupyter Notebooks for Machine Learning
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Day 1 - Hugging Face Integration with Google Colab: Secrets and API Keys Setup
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Day 1 - Mastering Google Colab: Run Open-Source AI Models with Hugging Face
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Day 2 - Hugging Face Transformers: Using Pipelines for AI Tasks in Python
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Day 2 - Hugging Face Pipelines: Simplifying AI Tasks with Transformers Library
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Day 2 - Mastering HuggingFace Pipelines: Efficient AI Inference for ML Tasks
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Day 3 - Exploring Tokenizers in Open-Source AI: Llama, Phi-2, Qwen, & Starcoder
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Day 3 - Tokenization Techniques in AI: Using AutoTokenizer with LLAMA 3.1 Model
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Day 3 - Comparing Tokenizers: Llama, PHI-3, and QWEN2 for Open-Source AI Models
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Day 3 - Hugging Face Tokenizers: Preparing for Advanced AI Text Generation
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Day 4 - Hugging Face Model Class: Running Inference on Open-Source AI Models
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Day 4 - Hugging Face Transformers: Loading & Quantizing LLMs with Bits & Bytes
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Day 4 - Hugging Face Transformers: Generating Jokes with Open-Source AI Models
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Day 4 - Mastering Hugging Face Transformers: Models, Pipelines, and Tokenizers
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Day 5 - Combining Frontier & Open-Source Models for Audio-to-Text Summarization
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Day 5 - Using Hugging Face & OpenAI for AI-Powered Meeting Minutes Generation
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Day 5 - Build a Synthetic Test Data Generator: Open-Source AI Model for Business
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Day 1 - How to Choose the Right LLM: Comparing Open and Closed Source Models
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Day 1 - Chinchilla Scaling Law: Optimizing LLM Parameters and Training Data Size
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Day 1 - Limitations of LLM Benchmarks: Overfitting and Training Data Leakage
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Day 1 - Evaluating Large Language Models: 6 Next-Level Benchmarks Unveiled
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Day 1 - HuggingFace OpenLLM Leaderboard: Comparing Open-Source Language Models
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Day 1 - Master LLM Leaderboards: Comparing Open Source and Closed Source Models
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Day 2 - Comparing LLMs: Top 6 Leaderboards for Evaluating Language Models
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Day 2 - Specialized LLM Leaderboards: Finding the Best Model for Your Use Case
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Day 2 - LLAMA vs GPT-4: Benchmarking Large Language Models for Code Generation
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Day 2 - Human-Rated Language Models: Understanding the LM Sys Chatbot Arena
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Day 2 - Commercial Applications of Large Language Models: From Law to Education
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Day 2 - Comparing Frontier and Open-Source LLMs for Code Conversion Projects
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Day 3 - Leveraging Frontier Models for High-Performance Code Generation in C++
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Day 3 - Comparing Top LLMs for Code Generation: GPT-4 vs Claude 3.5 Sonnet
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Day 3 - Optimizing Python Code with Large Language Models: GPT-4 vs Claude 3.5
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Day 3 - Code Generation Pitfalls: When Large Language Models Produce Errors
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Day 3 - Blazing Fast Code Generation: How Claude Outperforms Python by 13,000x
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Day 3 - Building a Gradio UI for Code Generation with Large Language Models
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Day 3 - Optimizing C++ Code Generation: Comparing GPT and Claude Performance
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Day 3 - Comparing GPT-4 and Claude for Code Generation: Performance Benchmarks
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Day 4 - Open Source LLMs for Code Generation: Hugging Face Endpoints Explored
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Day 4 - How to Use HuggingFace Inference Endpoints for Code Generation Models
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Day 4 - Integrating Open-Source Models with Frontier LLMs for Code Generation
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Day 4 - Comparing Code Generation: GPT-4, Claude, and CodeQuen LLMs
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Day 4 - Mastering Code Generation with LLMs: Techniques and Model Selection
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Day 5 - Evaluating LLM Performance: Model-Centric vs Business-Centric Metrics
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Day 5 - Mastering LLM Code Generation: Advanced Challenges for Python Developers
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Day 1 - RAG Fundamentals: Leveraging External Data to Improve LLM Responses
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Day 1 - Building a DIY RAG System: Implementing Retrieval-Augmented Generation
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Day 1 - Understanding Vector Embeddings: The Key to RAG and LLM Retrieval
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Day 2 - Unveiling LangChain: Simplify RAG Implementation for LLM Applications
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Day 2 - LangChain Text Splitter Tutorial: Optimizing Chunks for RAG Systems
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Day 2 - Preparing for Vector Databases: OpenAI Embeddings and Chroma in RAG
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Day 3 - Mastering Vector Embeddings: OpenAI and Chroma for LLM Engineering
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Day 3 - Visualizing Embeddings: Exploring Multi-Dimensional Space with t-SNE
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Day 3 - Building RAG Pipelines: From Vectors to Embeddings with LangChain
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Day 4 - Implementing RAG Pipeline: LLM, Retriever, and Memory in LangChain
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Day 4 - Mastering Retrieval-Augmented Generation: Hands-On LLM Integration
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Day 4 - Master RAG Pipeline: Building Efficient RAG Systems
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Day 5 - Optimizing RAG Systems: Troubleshooting and Fixing Common Problems
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Day 5 - Switching Vector Stores: FAISS vs Chroma in LangChain RAG Pipelines
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Day 5 - Demystifying LangChain: Behind-the-Scenes of RAG Pipeline Construction
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Day 5 - Debugging RAG: Optimizing Context Retrieval in LangChain
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Day 5 - Build Your Personal AI Knowledge Worker: RAG for Productivity Boost
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Day 1 - Fine-Tuning Large Language Models: From Inference to Training
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Day 1 - Finding and Crafting Datasets for LLM Fine-Tuning: Sources & Techniques
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Day 1 - Data Curation Techniques for Fine-Tuning LLMs on Product Descriptions
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Day 1 - Optimizing Training Data: Scrubbing Techniques for LLM Fine-Tuning
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132
Day 1 - Evaluating LLM Performance: Model-Centric vs Business-Centric Metrics
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Day 2 - LLM Deployment Pipeline: From Business Problem to Production Solution
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Day 2 - Prompting, RAG, and Fine-Tuning: When to Use Each Approach
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Day 2 - Productionizing LLMs: Best Practices for Deploying AI Models at Scale
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Day 2 - Optimizing Large Datasets for Model Training: Data Curation Strategies
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Day 2 - How to Create a Balanced Dataset for LLM Training: Curation Techniques
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Day 2 - Finalizing Dataset Curation: Analyzing Price-Description Correlations
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Day 2 - How to Create and Upload a High-Quality Dataset on HuggingFace
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Day 3 - Feature Engineering and Bag of Words: Building ML Baselines for NLP
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Day 3 - Baseline Models in ML: Implementing Simple Prediction Functions
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Day 3: Feature Engineering Techniques for Amazon Product Price Prediction Models
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Day 3 - Optimizing LLM Performance: Advanced Feature Engineering Strategies
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Day 3 - Linear Regression for LLM Fine-Tuning: Baseline Model Comparison
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Day 3 - Bag of Words NLP: Implementing Count Vectorizer for Text Analysis in ML
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146
Day 3 - Support Vector Regression vs Random Forest: Machine Learning Face-Off
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Day 3 - Comparing Traditional ML Models: From Random to Random Forest
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Day 4 - Evaluating Frontier Models: Comparing Performance to Baseline Frameworks
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Day 4 - Human vs AI: Evaluating Price Prediction Performance in Frontier Models
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Day 4 - GPT-4o Mini: Frontier AI Model Evaluation for Price Estimation Tasks
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Day 4 - Comparing GPT-4 and Claude: Model Performance in Price Prediction Tasks
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Day 4 - Frontier AI Capabilities: LLMs Outperforming Traditional ML Models
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Day 5 - Fine-Tuning LLMs with OpenAI: Preparing Data, Training, and Evaluation
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Day 5 - How to Prepare JSONL Files for Fine-Tuning Large Language Models (LLMs)
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Day 5 - Step-by-Step Guide: Launching GPT Fine-Tuning Jobs with OpenAI API
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Day 5 - Fine-Tuning LLMs: Track Training Loss & Progress with Weights & Biases
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Day 5 - Evaluating Fine-Tuned LLMs Metrics: Analyzing Training & Validation Loss
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Day 5 - LLM Fine-Tuning Challenges: When Model Performance Doesn't Improve
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Day 5 - Fine-Tuning Frontier LLMs: Challenges & Best Practices for Optimization
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160
Day 1 - Mastering Parameter-Efficient Fine-Tuning: LoRa, QLoRA & Hyperparameters
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161
Day 1 - Introduction to LoRA Adaptors: Low-Rank Adaptation Explained
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Day 1 - QLoRA: Quantization for Efficient Fine-Tuning of Large Language Models
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Day 1 - Optimizing LLMs: R, Alpha, and Target Modules in QLoRA Fine-Tuning
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Day 1 - Parameter-Efficient Fine-Tuning: PEFT for LLMs with Hugging Face
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Day 1 - How to Quantize LLMs: Reducing Model Size with 8-bit Precision
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Day 1: Double Quantization & NF4: Advanced Techniques for 4-Bit LLM Optimization
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Day 1 - Exploring PEFT Models: The Role of LoRA Adapters in LLM Fine-Tuning
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Day 1 - Model Size Summary: Comparing Quantized and Fine-Tuned Models
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169
Day 2 - How to Choose the Best Base Model for Fine-Tuning Large Language Models
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Day 2 - Selecting the Best Base Model: Analyzing HuggingFace's LLM Leaderboard
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Day 2 - Exploring Tokenizers: Comparing LLAMA, QWEN, and Other LLM Models
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Day 2 - Optimizing LLM Performance: Loading and Tokenizing Llama 3.1 Base Model
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Day 2 - Quantization Impact on LLMs: Analyzing Performance Metrics and Errors
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Day 2 - Comparing LLMs: GPT-4 vs LLAMA 3.1 in Parameter-Efficient Tuning
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Day 3 - QLoRA Hyperparameters: Mastering Fine-Tuning for Large Language Models
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Day 3 - Understanding Epochs and Batch Sizes in Model Training
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Day 3 - Learning Rate, Gradient Accumulation, and Optimizers Explained
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Day 3 - Setting Up the Training Process for Fine-Tuning
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Day 3 - Configuring SFTTrainer for 4-Bit Quantized LoRA Fine-Tuning of LLMs
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Day 3 - Fine-Tuning LLMs: Launching the Training Process with QLoRA
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Day 3 - Monitoring and Managing Training with Weights & Biases
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Day 4 - Keeping Training Costs Low: Efficient Fine-Tuning Strategies
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Day 4 - Efficient Fine-Tuning: Using Smaller Datasets for QLoRA Training
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Day 4 - Visualizing LLM Fine-Tuning Progress with Weights and Biases Charts
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Day 4 - Advanced Weights & Biases Tools and Model Saving on Hugging Face
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Day 4 - End-to-End LLM Fine-Tuning: From Problem Definition to Trained Model
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Day 5 - The Four Steps in LLM Training: From Forward Pass to Optimization
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Day 5 - QLoRA Training Process: Forward Pass, Backward Pass and Loss Calculation
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Day 5 - Understanding Softmax and Cross-Entropy Loss in Model Training
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Day 5 - Monitoring Fine-Tuning: Weights & Biases for LLM Training Analysis
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Day 5 - Revisiting the Podium: Comparing Model Performance Metrics
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Day 5 - Evaluation of our Proprietary, Fine-Tuned LLM against Business Metrics
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193
Day 5 - Visualization of Results: Did We Beat GPT-4?
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Day 5 - Hyperparameter Tuning for LLMs: Improving Model Accuracy with PEFT
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Day 1 - From Fine-Tuning to Multi-Agent Systems: Next-Level LLM Engineering
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Day 1: Building a Multi-Agent AI Architecture for Automated Deal Finding Systems
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197
Day 1 - Unveiling Modal: Deploying Serverless Models to the Cloud
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Day 1 - LLAMA on the Cloud: Running Large Models Efficiently
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Day 1 - Building a Serverless AI Pricing API: Step-by-Step Guide with Modal
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Day 1 - Multiple Production Models Ahead: Preparing for Advanced RAG Solutions
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Day 2 - Implementing Agentic Workflows: Frontier Models and Vector Stores in RAG
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Day 2 - Building a Massive Chroma Vector Datastore for Advanced RAG Pipelines
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Day 2 - Visualizing Vector Spaces: Advanced RAG Techniques for Data Exploration
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Day 2 - 3D Visualization Techniques for RAG: Exploring Vector Embeddings
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Day 2 - Finding Similar Products: Building a RAG Pipeline without LangChain
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Day 2 - RAG Pipeline Implementation: Enhancing LLMs with Retrieval Techniques
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Day 2 - Random Forest Regression: Using Transformers & ML for Price Prediction
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Day 2 - Building an Ensemble Model: Combining LLM, RAG, and Random Forest
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Day 2 - Wrap-Up: Finalizing Multi-Agent Systems and RAG Integration
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Day 3 - Enhancing AI Agents with Structured Outputs: Pydantic & BaseModel Guide
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Day 3 - Scraping RSS Feeds: Building an AI-Powered Deal Selection System
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Day 3 - Structured Outputs in AI: Implementing GPT-4 for Detailed Deal Selection
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Day 3 - Optimizing AI Workflows: Refining Prompts for Accurate Price Recognition
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214
Day 3 - Mastering Autonomous Agents: Designing Multi-Agent AI Workflows
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Day 4 - The 5 Hallmarks of Agentic AI: Autonomy, Planning, and Memory
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Day 4 - Building an Agentic AI System: Integrating Pushover for Notifications
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Day 4 Implementing Agentic AI: Creating a Planning Agent for Automated Workflows
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Day 4 - Building an Agent Framework: Connecting LLMs and Python Code
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Day 4 - Completing Agentic Workflows: Scaling for Business Applications
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Day 5 - Autonomous AI Agents: Building Intelligent Systems Without Human Input
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Day 5 - AI Agents with Gradio: Advanced UI Techniques for Autonomous Systems
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Day 5 - Finalizing the Gradio UI for Our Agentic AI Solution
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Day 5 Enhancing AI Agent UI: Gradio Integration for Real-Time Log Visualization
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Day 5 - Analyzing Results: Monitoring Agent Framework Performance
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Day 5 - AI Project Retrospective: 8-Week Journey to Becoming an LLM Engineer
