Comprehensive AI Engineering Bootcamp for Full Stack Engineers

Join me, Reed Mayo, as we embark on an AI engineering boot camp, covering the fundamentals of large language models, prompt engineering, OpenAI, Lang chain, and fine-tuning, designed to help full-stack engineers like you launch a professional AI career.

1. Reed Mayo, founder of Rema AI, will discuss how to become an AI engineer from a full stack background.
2. The talk assumes a strong full stack engineering background but zero background in AI or machine learning.
3. The rise of the AI engineer by Shan Swix Wang provides insights into why one would be interested in becoming an AI engineer.
4. Full stack engineers can now deploy a wide variety of useful AI solutions thanks to new foundational models and techniques.
5. This talk will provide a syllabus that walks you step by step through the process of launching an AI engineering career.
6. The course was designed around techniques from The Art of Learning, including staying focused and limiting distractions.
7. Fundamental building blocks will be heavily invested in to build sophisticated AI products through composition.
8. Use chat GPT as a private tutor and the Socratic method to unfurl new concepts until thoroughly understood.
9. Section one: Overview of large language models - Understand what they are and how they work at a high level.
10. Coher has a great overview of Core Concepts in their educational docs, so stay focused on module one.
11. Section two: Prompt engineering - Learn about prompting AI models politely to increase the quality of output.
12. Fine-tuning quality is often increased by starting with the best performing prompts and using them in your fine-tuning training data.
13. Syn hands into the prompt engineering clay to see what language models are capable of and probe their limitations.
14. Section three: Open AI - Understand state-of-the-art AI models and how to make them accessible.
15. Read Open AI docs and API reference cover to cover, then quickly review Practical Hands-On examples in the cookbook.
16. Section four: Lang chain - Learn about the application's framework for modern AI systems.
17. Build a comprehensive understanding of Lang chain as it is the glue layer for most everything else in the AI ecosystem.
18. Section five: Evaluating AI models - Understand how to evaluate changes iteratively before fine-tuning blackbox AI models.
19. Use Open AI's cookbook and robust eval suite to write custom evals and review materials quickly.
20. Section six: Fine-tuning - Learn how to fine-tune open AI models step by step from the cookbook.
21. Prototype and ship a solution quickly using Open AI's models, then gather usage and training data for scaling.
22. Consider fine-tuning a smaller and cheaper open source model if solutions need to scale.
23. Final section: Advanced study - Deploy AI engineering skills in the real world before moving onto advanced studies.
24. Fast ai's practical deep learning course, Hugging Face's NLP course, and their docs will give a rich understanding of deep learning theory.

Source: AI Engineer via YouTube

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