▶ Best Intro Book for MLOps

In this video, I review "AI Engineering: Building Applications with Foundation Models" by Chip Huyen, one of the best books for learning how to build real-world AI applications using LLMs, RAG & foundation models.This is not a theoretical machine learning book. This is a practical guide to AI Engineering - covering prompt engineering, retrieval-augmented generation (RAG), model evaluation, fine-tuning, deployment, latency optimization, and production AI systems. If you are a Software engineer transitioning to AI, Machine learning engineer, Data scientist, Student preparing for AI careers, Developer building LLM apps. 📚 What You’ll Learn in This Video : ▶ What AI Engineering really means ▶ Difference between ML Engineering vs AI Engineering ▶ How foundation models like GPT are used in production ▶ How to build LLM applications ▶ RAG (Retrieval-Augmented Generation) explained ▶ Prompt engineering best practices ▶ Evaluating and scaling AI systems ▶ Why this book is different from other AI books Who Should Watch This? If you are serious about : Becoming an AI Engineer, Building AI startups, Understanding how ChatGPT-like systems are built, Learning practical AI system design, This video will help you decide whether this book is worth your time. 📖 Book Details: Title: AI Engineering: Building Applications with Foundation Models Author: Chip Huyen Publisher: O’Reilly ▶ Sponsor me on GitHub : https://github.com/sponsors/bhattbhavesh91/ ▶ Join this channel to get access to perks: https://bit.ly/BhaveshBhattJoin ▶ Join the Telegram channel for regular updates: https://t.me/bhattbhavesh91 ▶ If you like my work, you can buy me a coffee : https://bit.ly/BuyBhaveshCoffee *I use affiliate links on the products that I recommend. These give me a small portion of the sales price at no cost to you. I appreciate the proceeds and they help me to improve my channel! ▶ Best Book for Python : https://amzn.to/3qYThqu ▶ Best Book for PyTorch & Machine Learning : https://amzn.to/3PyUkdy ▶ Best Book for Statistics : https://amzn.to/3vzvHEn ▶ Best Book for BERT: https://amzn.to/3lpX0fz ▶ Best Book for Machine Learning : https://amzn.to/2P6aZuT ▶ Best Book for Deep Learning : https://amzn.to/30UMTGl ▶ Best Intro Book for MLOps : https://amzn.to/3AoPZmM Equipments I use for recording the videos: ▶ 1st Laptop I use : https://amzn.to/3AqI8Fp ▶ 2nd Laptop I use : https://amzn.to/3KAiYsB ▶ Microphone : https://amzn.to/3qUPxtz ▶ Camera : https://amzn.to/3rKQsM2 ▶ Mobile Phone : https://amzn.to/3nRHP1f ▶ Ring Light : https://amzn.to/33LedM5 ▶ RGB Light : https://amzn.to/3KzLgmS ▶ Bag I use : https://amzn.to/3AsM3RZ If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: ▶ Blog - https://bhattbhavesh91.github.io ▶ Twitter - https://twitter.com/_bhaveshbhatt ▶ GitHub - https://github.com/bhattbhavesh91 ▶ Medium - https://medium.com/@bhattbhavesh91 ▶ About.me - https://about.me/bhattbhavesh91 ▶ Linktree - https://linktr.ee/bhattbhavesh91 ▶ DEV Community - https://dev.to/bhattbhavesh91 ▶ Telegram - https://t.me/bhattbhavesh91 #aiengineering #llm