Lecture Notes: Generative AI Essentials Course Introduction

Course Overview

• Instructor: Andrew Brown
• Aim: Comprehensive knowledge for building generative AI applications
• Prerequisite for the free "Gen Boot Camp" for hands-on project building
• Focus on building generative AI applications

Certification Information

• Genis Essentials Certification: Practical certification covering fundamental concepts of ML, AI, generative models with a focus on LLMs
• Course Code: EXP-GENI-001
• Vendor Agnostic
  • Not specific to AWS, Azure, or Nvidia
  • Fills knowledge gaps left by vendor-specific courses
• Continuous Updates: Regular updates expected as the field evolves

Target Audience

• Individuals preparing for the Gen Boot Camp
• Those needing broad, practical knowledge on generative solutions
• Technical flexibility across cloud services and local implementations

Course Goals

• Provide a foundational understanding of generative AI solutions
• Cover aspects like cloud services, local LLMs, fine-tuning, RAG, cost, and security
• Emphasis on implementation and hands-on development
• Prepare students to tackle real-world problems using generative AI technologies

Course Roadmap

• Certification Course -> Project-Based Boot Camp (Free Gen Boot Camp)
• Study time: 15-30 hours
• Average study recommendation: 1-2 hours/day for 15 days
• Optional certification with potential costs

Examination Details

• Passing Score: 75%
• Format: Multiple choice, multiple answer, case studies
• Duration: 2 hours
• Online supervised exams available
• Validity: Indefinite, but recertification might be necessary with major updates

Learning Model and Roadmap

• ML Basics: Introduction to ML concepts
• Generative AI Introduction: Covers different generative tasks like text, video, image, etc.
• AI-powered Assistants: Use of generative AI in building conversational agents
• Programmatic APIs: Working with APIs to integrate AI solutions into applications
• Developer Tools: Tools like Hugging Face, LangChain, and others for model deployment
• Security: Overview of security practices in AI deployments
• Hardware Requirements: Understanding hardware considerations for deploying AI models
• Advanced Techniques: Fine-tuning, evaluation, and optimization strategies

Conclusion

• Generative AI Essentials aims to equip students with the capability to build and deploy generative AI applications across various platforms and use cases.
• Emphasis on practical, hands-on learning to ensure readiness for emerging AI challenges.

Additional Resources

• Keep an eye on updates, new versions, and additional material as the AI field rapidly evolves.
• Engage with online communities or study groups for additional support and resources.