├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Javin Paul 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Best AI and LLM Engineering Resources 2 | 3 | Welcome to **Best AI and LLM Engineering Resources** — a curated collection of high-quality books, courses, and learning materials to help software engineers, data scientists, and AI enthusiasts master **Large Language Models (LLMs)**, **Prompt Engineering**, **AI System Design**, and **Machine Learning Engineering**. 4 | 5 | This repository aims to provide reliable resources that can guide you in building, deploying, and optimising LLMs and AI systems for production. 6 | 7 | --- 8 | 9 | ## 📚AI and LLM Engineering Books 10 | 11 | Books are gret way to start your AI journey, especially if you want to transition form Software Engineer to AI Engineer. Here are **10 must-read AI and LLM engineering books** for developers: 12 | 1. [The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne](https://buff.ly/wogklbo) 13 | 2. [AI Engineering by Chip Huyen](https://buff.ly/Nf1RMHU) 14 | 3. [Designing Machine Learning Systems by Chip Huyen](https://buff.ly/EfN5uOE) 15 | 4. [Building LLMs for Production by Louis-François Bouchard and Louie Peters](https://buff.ly/wjpOeTB) 16 | 5. [Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD](https://buff.ly/DHp4ZR1) 17 | 6. [Hands-On Large Language Models: Language Understanding and Generation](https://buff.ly/WInCgwi) 18 | 7. [Prompt Engineering for LLMs](https://buff.ly/oqsrvvw) 19 | 8. [Building Agentic AI Systems](https://buff.ly/6vnnXgl) 20 | 9. [Prompt Engineering for Generative AI](https://buff.ly/vgk6RZ4) 21 | 10. [The AI Engineering Bible](https://buff.ly/8LQipeQ) 22 | 23 | 24 | --- 25 | ## 📘 AI and LLM Engineering Courses 26 | ### Udemy 27 | 1. [LLM Engineering: Master AI, Large Language Models & Agents](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fllm-engineering-master-ai-and-large-language-models%2F%3FcouponCode%3DST4MT240225A) 28 | 2. [LangChain- Develop LLM-powered applications with LangChain](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Flangchain%2F%3FcouponCode%3DST4MT240225A) 29 | 3. [Complete Generative AI Course With Langchain and Huggingface](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fcomplete-generative-ai-course-with-langchain-and-huggingface%2F%3FcouponCode%3DST4MT240225A) 30 | 4. [Artificial Intelligence A-Z™: Learn How To Build An AI](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fartificial-intelligence-az%2F&u1=javarevisited) 31 | 5. [The Complete Artificial Intelligence and ChatGPT Course](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fthe-complete-artificial-intelligence-and-chat-gpt-course%2F&u1=javarevisited) 32 | 6. [Machine Learning A-Z™: Hands-On Python & R In Data Science](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fmachinelearning%2F&u1=javarevisited) 33 | 7. [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fdeeplearning%2F&u1=javarevisited) 34 | 8. [Generative AI for Beginners](https://click.linksynergy.com/deeplink?id=CuIbQrBnhiw&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fgenerative-ai-for-beginners-b%2F%3FcouponCode%3DLEADERSALE24A) 35 | 9. [Open-source LLMs: Uncensored & secure AI locally with RAG](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fopen-source-llms-uncensored-secure-ai-locally-with-rag%2F%3FcouponCode%3DST4MT240225A) 36 | 10. [AI-Agents: Automation & Business with LangChain & LLM Apps](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourse%2Fai-agents-automation-business-with-langchain-llm-apps%2F%3FcouponCode%3DST4MT240225A) 37 | 38 | 39 | ### Educative 40 | 1. [Become an LLM Engineer](https://www.educative.io/path/become-an-llm-engineer?aff=VMMr) (Skill Path | Best for Engineers & Developers) 41 | 2. [Grokking AI for Engineering & Product Managers](https://www.educative.io/courses/grokking-ai-for-engineering-product-managers?aff=VMMr) (Best for Tech Leads and PMs) 42 | 3. [Generative AI Essentials](https://www.educative.io/courses/generative-ai-essentials?aff=VMMr) (Best for Beginners & Tech Enthusiasts) 43 | 4. [Generative AI Essentials](https://www.educative.io/courses/generative-ai-essentials?aff=VMMr) (Best for Beginners & Tech Enthusiasts) 44 | 5. [Code Smarter with Cursor AI Editor](https://www.educative.io/courses/cursor-ai?aff=VMMr) (Best for VS Code Users & Productivity-Focused Developers) 45 | 6. [Become an Agentic AI Expert](https://www.educative.io/path/become-an-agentic-ai-expert?aff=VMMr) (New Skill Path) 46 | 47 | ### Couresra 48 | Here are some of the best courses to master AI, LLMs, and their applications: 49 | 50 | - [Deep Learning Specialization – Andrew Ng (Coursera)](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) 51 | A foundational deep learning series covering neural networks, CNNs, RNNs, and more. 52 | 53 | - [Prompt Engineering for ChatGPT (DeepLearning.AI)](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprompt-engineering) 54 | A practical introduction to prompt engineering and working with language models. 55 | 56 | - [Generative AI with Large Language Models](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fgenerative-ai-with-llms) 57 | Learn how to apply LLMs in real-world products and services. 58 | 59 | - [Natural Language Processing - Transformers with Hugging Face](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fpackt-natural-language-processing-transformers-with-hugging-face-ydvmi) 60 | A focused course on transformers and building NLP solutions. 61 | 62 | - [Natural Language Processing Specialization (Coursera)](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fnatural-language-processing) 63 | Covers core NLP techniques and how to build applications using them. 64 | 65 | - [TensorFlow Developer Professional Certificate (Coursera)](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Ftensorflow-in-practice) 66 | Master TensorFlow and deep learning to build production-ready AI systems. 67 | 68 | --- 69 | 70 | ## YouTube Videos to Learn LLM and Agentic AI 71 | 72 | 1. LLM Introduction: https://www.youtube.com/watch?v=zjkBMFhNj_g 73 | 74 | 2. LLMs from Scratch: https://www.youtube.com/watch?v=9vM4p9NN0Ts 75 | 76 | 3. Agentic AI Overview (Stanford): https://www.youtube.com/watch?v=kJLiOGle3Lw 77 | 78 | 4. Building and Evaluating Agents: https://www.youtube.com/watch?v=d5EltXhbcfA 79 | 80 | 5. Building Effective Agents: https://www.youtube.com/watch?v=D7_ipDqhtwk 81 | 82 | 6. Building Agents with MCP: https://www.youtube.com/watch?v=kQmXtrmQ5Zg 83 | 84 | 7. Building an Agent from Scratch: https://www.youtube.com/watch?v=xzXdLRUyjUg 85 | 86 | 8. Philo Agents: https://www.youtube.com/playlist?list=PLacQJwuclt_sV-tfZmpT1Ov6jldHl30NR 87 | 88 | --- 89 | 90 | 91 | ## 🌐 Best Places to Learn AI and LLM 92 | 93 | If you are looking for platforms that offer comprehensive AI and LLM learning materials, explore these: 94 | 95 | - [Coursera](https://coursera.pxf.io/c/3294490/1164545/14726?u=https%3A%2F%2Fwww.coursera.org%2F) 96 | University-level AI/ML/LLM courses, specialisations, and professional certificates. 97 | 98 | - [DeepLearning.AI](https://www.deeplearning.ai) 99 | Specialises in AI/LLM courses by leading practitioners, including Andrew Ng. 100 | 101 | - [Hugging Face](https://huggingface.co/learn) 102 | The best place to learn practical NLP, transformers, and LLMs from the creators of leading open-source libraries. 103 | 104 | - [Udacity](https://click.linksynergy.com/deeplink?id=JVFxdTr9V80&mid=53187&murl=https%3A%2F%2Fwww.udacity.com%2Fschool-of-ai) 105 | Offers AI nanodegree programs focusing on deep learning, NLP, and production AI systems. 106 | 107 | - [Fast.ai](https://course.fast.ai) 108 | A free, practical deep learning course that teaches how to build and deploy models efficiently. 109 | 110 | - [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index) 111 | Go-to resource for learning how to use LLMs and transformers in code. 112 | 113 | --- 114 | ## AI and LLM Engineering Articles 115 | - [The Complete AI and LLM Engineering Roadmap: From Beginner to Expert](https://javarevisited.substack.com/p/the-complete-ai-and-llm-engineering) 116 | - [AI Fundamentals - Vector Database](https://javarevisited.substack.com/p/ai-fundamentals-vector-databases) 117 | - [5 Books to Master Agentic AI and LLM Engineering by Paul Iustzin- author of LLM Engineering Handbook](https://javarevisited.substack.com/p/5-books-to-master-agentic-ai-and?utm_source=publication-search) 118 | - [6 Generative AI Courses to learn LLM, ChatGPT, and LangChain](https://javarevisited.substack.com/p/6-generative-ai-courses-to-learn) 119 | - [The 3P Architecture: A Deep Dive into Software Agent Design (with Manus AI)](https://javarevisited.substack.com/p/the-3p-architecture-a-deep-dive-into?utm_source=publication-search) 120 | - [RAG Fundamentals: Getting Started with Retrieval-Augmented Generation](https://javarevisited.substack.com/p/rag-fundamentals-getting-started?utm_source=publication-search) 121 | - [Monolith vs Microservices: The $1M ML Design Decision](https://javarevisited.substack.com/p/monolith-vs-microservices-the-1m?utm_source=publication-search) 122 | - [8 Videos You Need to Understand AI Agents (and the Resources I Wish I Had Earlier)](https://medium.com/javarevisited/8-videos-you-need-to-understand-ai-agents-and-the-resources-i-wish-i-had-earlier-93988651460a) 123 | - [How to Crack AI/ML/GenAI Interviews?](https://medium.com/javarevisited/how-to-crack-ai-ml-genai-interviews-in-2025-5347c6057d50) 124 | - [Top 5 Vector Databases to Learn (with Courses and Books to Master Them)](https://medium.com/javarevisited/top-5-vector-databases-to-learn-in-2025-with-courses-and-books-to-master-them-a555e7154909) 125 | - [Top 10 Agentic AI Courses for Beginners & Experienced](https://medium.com/javarevisited/top-10-agentic-ai-courses-for-beginners-experienced-in-2025-6ea7578346f0) 126 | - [Top 9 Books to Learn RAG and AI Agents](https://javinpaul.medium.com/top-9-books-to-learn-rag-and-ai-agents-in-2025-e5fc54f44746) 127 | - [How I’m Learning Machine Learning and AI](https://medium.com/javarevisited/how-im-learning-machine-learning-and-ai-76c964d34fe5) 128 | - [From Zero to AI Engineer: A 5-Step Roadmap to Build and Ship Real AI Systems ](https://medium.com/javarevisited/from-zero-to-ai-engineer-a-5-step-roadmap-to-build-and-ship-real-ai-systems-in-2025-28f866d6f8b7) 129 | 130 | --- 131 | 132 | ## 🚀 Best AI and LLM Projects (Ideas to Build & Learn) 133 | 134 | Building projects is the best way to cement your learning. Here are some small to medium-sized project ideas to try: 135 | 136 | ### Beginner Projects 137 | - **Sentiment Analysis Tool** 138 | Build a tool to detect sentiment (positive/negative/neutral) from tweets or product reviews using transformers or classical NLP. 139 | 140 | - **AI-Powered Text Summariser** 141 | Use models like T5 or BART to summarise long articles into a few sentences. 142 | 143 | - **Chatbot with GPT-4 API** 144 | Create a basic conversational bot using OpenAI’s API for fun or customer support use cases. 145 | 146 | --- 147 | 148 | ### Intermediate Projects 149 | - **Question Answering System** 150 | Build a system that can answer user queries based on a knowledge base or documents using LLMs. 151 | 152 | - **AI Resume Screener** 153 | Develop a tool that analyses resumes and provides summaries/highlights based on job descriptions. 154 | 155 | - **Voice-to-Text Transcription using Whisper** 156 | Use OpenAI’s Whisper model to convert speech from audio files into text. 157 | 158 | --- 159 | 160 | ### Advanced Projects 161 | - **Multi-modal AI App (Text + Image)** 162 | Combine text and image inputs (like BLIP-2) to create a smart captioning or Q&A system. 163 | 164 | - **Custom Fine-tuned LLM** 165 | Fine-tune a pre-trained language model on domain-specific data (e.g., legal, medical) and deploy via API. 166 | 167 | - **End-to-End AI Search Engine** 168 | Build a mini search engine using embedding techniques (e.g., vector DBs + OpenAI embeddings) for semantic search. 169 | 170 | ----- 171 | 172 | ## Prompt Engineering Courses 173 | Here are some of the best Udemy courses to start with: 174 | 175 | - [The Complete Prompt Engineering for AI Bootcamp (2025)](https://buff.ly/CH3kZG5) 176 | - [ChatGPT Complete Guide: Learn Midjourney, ChatGPT 4 & More](https://buff.ly/K2s4hsk) 177 | - [Complete ChatGPT Prompt Engineering Course](https://buff.ly/3EpuPE4) 178 | - [Natural Language Processing with Transformers [Udemy]](https://buff.ly/rJr7cfl) 179 | - [GPT-4 Masterclass: Build World-Class AI Language Models](https://buff.ly/9mHXHIG) 180 | 181 | --- 182 | 183 | ## TensorFlow Learning Resources 184 | 185 | Here are some of the best TensorFlow courses and certifications to join: 186 | 187 | - [Complete Guide to TensorFlow for Deep Learning with Python](https://buff.ly/3Zrw59h) 188 | - [Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning](https://buff.ly/4auRtw5) 189 | - [Deep Learning with TensorFlow 2.0](https://buff.ly/3TrOnn6) 190 | - [Machine Learning in JavaScript with TensorFlow.js](https://buff.ly/3Zo6XQJ) 191 | - [TensorFlow 2.0: Deep Learning and Artificial Intelligence](https://buff.ly/4daXtvj) 192 | - [TensorFlow Developer Certificate](https://buff.ly/3Bf0MnW) 193 | - [More TensorFlow Resources](https://buff.ly/3TQA7oj) 194 | 195 | --- 196 | 197 | ## 💡 Best AI and LLM Engineering Interview Questions 198 | 199 | Prepare for interviews with these commonly asked AI and LLM engineering questions: 200 | 201 | - Explain the architecture of a transformer model. 202 | - What are positional encodings and why are they important in transformers? 203 | - How would you fine-tune a large language model on a domain-specific dataset? 204 | - Discuss techniques for reducing hallucination in LLM outputs. 205 | - What are tokenizers? How do you choose one for your application? 206 | - Compare zero-shot, few-shot, and fine-tuning approaches for LLMs. 207 | - How do you evaluate the performance of an LLM-based system? 208 | - What are the trade-offs between pre-training and prompt engineering? 209 | - Describe challenges in deploying LLMs at scale. 210 | - How would you handle latency issues in a production LLM API? 211 | 212 | --- 213 | 214 | ## 🏗️ System Design for AI Engineering 215 | 216 | When designing AI systems, these are key system design patterns and components to consider: 217 | 218 | - **Model Serving Infrastructure:** Design for high availability, low latency (e.g., TensorFlow Serving, TorchServe, custom API). 219 | - **Vector Databases for Embeddings:** Use tools like Pinecone, Weaviate, or Milvus for semantic search. 220 | - **Inference Optimisation:** Quantisation, pruning, distillation for faster inference. 221 | - **Feature Store:** A central repository for storing and sharing ML features (e.g., Feast, Tecton). 222 | - **Batch vs Real-time Inference:** Trade-offs between latency and throughput. 223 | - **Monitoring and Feedback Loop:** Continuous model monitoring, drift detection, and feedback for improvement. 224 | 225 | --- 226 | 227 | ## 📦 LLM Deployment Patterns 228 | 229 | Common patterns for deploying large language models effectively: 230 | 231 | - **API-as-a-Service:** Wrap your LLM as a REST/gRPC API and deploy using cloud services or Kubernetes. 232 | - **Edge Deployment:** Run small or quantised models on edge devices (e.g., smartphones, IoT devices). 233 | - **Hybrid Cloud + Edge:** Serve large models from the cloud and lightweight components on-device. 234 | - **Multi-Tenant Serving:** Serve multiple models or versions using a shared infrastructure (e.g., using Seldon or KServe). 235 | - **Sharded Serving:** Split large models across multiple nodes for parallel inference. 236 | - **Serverless LLM Inference:** Use serverless platforms (e.g., AWS Lambda + Hugging Face) for cost-efficient scaling. 237 | 238 | --- 239 | 240 | ## 🚀 Productionisation of AI Systems 241 | 242 | Things to consider when moving AI systems from prototype to production: 243 | 244 | - **Model Versioning and CI/CD:** Use tools like MLflow, DVC, or Weights & Biases for model tracking and deployment automation. 245 | - **Latency and Throughput:** Design APIs and infra for SLA adherence. 246 | - **Scalability:** Use autoscaling and load balancing (e.g., K8s, AWS SageMaker) for inference endpoints. 247 | - **Monitoring:** Set up metrics for input data drift, model performance degradation, and alerting. 248 | - **Data Privacy and Security:** Implement encryption at rest and in transit, role-based access control. 249 | - **A/B Testing and Shadow Deployment:** Safely roll out new models and gather feedback. 250 | 251 | --- 252 | 253 | ## 🛠️ Open-Source Libraries, Frameworks, and Toolkits 254 | 255 | Here are some essential open-source tools for AI and LLM engineering: 256 | 257 | - **Transformers (Hugging Face)** — Industry-standard library for using pre-trained NLP models 258 | [https://github.com/huggingface/transformers](https://github.com/huggingface/transformers) 259 | 260 | - **LangChain** — Framework for building applications with LLMs, chaining models, and tools 261 | [https://github.com/langchain-ai/langchain](https://github.com/langchain-ai/langchain) 262 | 263 | - **MLflow** — Platform for managing the ML lifecycle (experiments, deployment, tracking) 264 | [https://github.com/mlflow/mlflow](https://github.com/mlflow/mlflow) 265 | 266 | - **TensorFlow** — Google’s open-source ML library for building and deploying models 267 | [https://github.com/tensorflow/tensorflow](https://github.com/tensorflow/tensorflow) 268 | 269 | - **PyTorch** — Flexible, widely used deep learning framework 270 | [https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch) 271 | 272 | - **Pinecone / Milvus / Weaviate** — Vector DBs for managing embeddings and similarity search 273 | [https://www.pinecone.io/](https://www.pinecone.io/) 274 | [https://milvus.io/](https://milvus.io/) 275 | [https://weaviate.io/](https://weaviate.io/) 276 | 277 | - **Seldon / KServe** — Open-source model serving and inference platforms for Kubernetes 278 | [https://github.com/SeldonIO/seldon-core](https://github.com/SeldonIO/seldon-core) 279 | [https://github.com/kserve/kserve](https://github.com/kserve/kserve) 280 | 281 | - **Weights & Biases (wandb)** — Experiment tracking, visualisation, and monitoring 282 | [https://github.com/wandb/client](https://github.com/wandb/client) 283 | 284 | --- 285 | ## [AI and LLM Engineering RoadMap](https://javarevisited.substack.com/p/the-complete-ai-and-llm-engineering) 286 | 287 | ![e8daef35-b630-4f56-90d0-7b27ed261823_1200x1200](https://github.com/user-attachments/assets/e8c642a8-46bb-4834-8533-700ab56e09d9) 288 | 289 | 290 | --- 291 | 292 | Feel free to contribute by submitting pull requests with high-quality resources! 293 | 294 | --- 295 | 296 | ## License 297 | 298 | This repository is licensed under the **MIT License** — see the `LICENSE` file for details. This means you are free to use, share, and build upon this work, provided you give appropriate credit. 299 | 300 | --- 301 | 302 | ## Contribution Guidelines 303 | 304 | If you know of a resource that fits this repository, kindly open a pull request. Please ensure that the resource is of high quality and preferably free or affordable for learners globally. 305 | 306 | --- 307 | 308 | --------------------------------------------------------------------------------