Beginner's Roadmap to Large Language Models (LLMOps) in 2023: All free!

The world has never evolved this fast. This is especially true in Artificial Intelligence, where Large Language Models (LLMs) are redefining the boundaries of possibility. Likewise for this new emerging field of LLMOps. My name is Louis, and today I want to share a curated list of resources I’ve built over time from the questions I got on YouTube for people wanting to get started in the field.

My original AI guide is full of resources for those starting in AI, packed with courses, books, and podcasts. As the AI field advances, so too has my perspective. In 2023, the focus shifts from theory to practical application, from learning algorithms to leveraging them in real-world scenarios.

This new one is more than just a guide; it's a roadmap for anyone with a tiny bit of Python skills, eager to dive into AI or enhance their existing skills. Whether you're new to programming or AI, my beginner’s guide is your first step. And for those already on the path, this updated guide serves as a compass, pointing you toward mastering LLMs.

LLMs are at the forefront of today's tech revolution. Understanding and utilizing these models is key to unlocking doors in AI-driven careers. This guide isn’t just a compilation of resources; it's a curated journey through the most valuable skills in the industry. It's about building a strong LLM-focused portfolio, whether for your next job, boosting productivity, or launching your own startup. And the best part? It's completely free.

I also encourage you to contribute, sharing any valuable LLM resources you come across. Please, reach out here or submit an issue on the GitHub repository with resources you used and found pertinent!

I put emphasis on sharing various learning modalities - from YouTube videos and podcasts for auditory learners to in-depth articles, books, and hands-on courses for those who prefer a more structured approach. This multi-modal learning experience is designed to cater to different learning styles, ensuring a comprehensive and engaging journey into the world of LLMs.

Here are the Best Resources from the Guide (to make you want to learn more!):

1. YouTube Videos: A selection of short, informative videos introducing LLM concepts and terminology (Letitia Coffee Break, ByCloud, What’s AI).
2. Podcasts: Recommendations include ‘Lex Fridman Podcast,’ ‘Machine Learning Street Talk,’ and ‘What’s AI by Louis Bouchard.’
3. Reading Materials: Articles and books, notably ‘The Illustrated Transformer’ by Jay Alammar.
4. Online Courses:
   • ‘Train & Fine-Tune LLMs for Production Course’ by Activeloop, Towards AI, and Intel Disruptor Initiative.
   • ‘LLM University’ by Cohere.
   • YouTube ‘Gradio Course’ by freeCodeCamp.
5. AI Communities: Join platforms like Discord, Slack, and Reddit for collaboration and knowledge exchange. The Learn AI Together community I built is an amazing one for Discord.
6. News and Updates: I shared a few highly relevant curated newsletters and YouTube channels for the latest in AI.

Some of the Learning Tips I shared in my video:

1. Practical Application: Emphasize building projects and applying what you learn.
2. Diverse Learning Modalities: Engage in listening, watching, reading, and doing for a comprehensive learning experience.
3. Community Engagement: Join AI communities for motivation, networking, and collaborative learning.
4. Continuous Practice: Regular hands-on practice to reinforce learning and skill development.
5. Stay Updated: Keep up with the latest AI developments through newsletters and YouTube channels.
6. Use Assistive Tools: Leverage tools like ChatGPT or GitHub Copilot for efficient learning and project development.
7. Explore Advanced Concepts: Learn Prompting and Retrieval Augmented Generation (RAG) techniques.
8. Balanced Learning: Balance structured courses with self-guided exploration and project work.
9. Don’t forget your mental health, even if you are super motivated. Sleep enough. Move enough. See friends and family enough!

Learn more about the guide and LLMOps in the video…

The full guide on GitHub: