AI Engineering Learning Roadmap

AI Engineering Learning Roadmap

We’re sharing a learning roadmap for those who want to start AI engineering. This roadmap is structured to allow step-by-step learning of the core areas of AI engineering.

👨‍💻 Coding & ML Fundamentals

Basic Languages and Concepts

Python

• Python for Data Science (freeCodeCamp)
• Basic Python syntax and data structures.
• Data science libraries like NumPy, Pandas, Matplotlib.

Bash

• Bash Scripting Crash Course
• Basic shell commands and scripting.
• Automation and system management.

Typescript (Optional)

• TypeScript Handbook
• Static type system.
• Object-oriented programming.

Statistics & Types of ML Models

• Khan Academy - Statistics and Probability
• Google’s Machine Learning Crash Course
• “The Hundred-Page Machine Learning Book” by Andriy Burkov

📦 LLM APIs

• OpenAI Cookbook
• LangChain Documentation
• Anthropic API Guide
• Multi-modal with OpenAI (vision & audio)

🔧 Model Adaptation

• Prompt Engineering Guide
• Hugging Face – Transformers Fine-tuning Course
• Toolformer: Language Models Can Teach Themselves to Use Tools

🗃️ Storage for Retrieval

• Pinecone Docs (Vector DB)
• Weaviate RAG Starter
• Redis + LangChain Hybrid Retrieval Tutorial

🧠 RAG & Agentic RAG

• RAG with LangChain and OpenAI
• Haystack RAG Pipeline
• Meta’s original RAG paper
• LLM Orchestration with LangGraph

🤖 AI Agents

• LangChain Agents
• Auto-GPT
• OpenAgents (OpenAI DevDay Demo)
• ReAct Paper

🏗️ Infrastructure

• MLOps Zoomcamp (free)
• Deploy LLM with Kubernetes (by Pinecone)
• CI/CD with GitHub Actions

👀 Observability & Evaluation

• WhyLogs (WhyLabs)
• TruLens for LLM Eval
• LangSmith (by LangChain)

🛡️ Security

• AI Red Teaming Guide (Microsoft)
• Ethical Considerations from DeepMind
• Llama Guard & Prompt Guardrails

🚀 Forward Looking Elements

• AI Agents for Robotics - Stanford CS327G
• Voice Cloning & TTS (coqui.ai)
• CLI AI Agents (Auto-GPT, gpt-engineer)

Learning Guide

This roadmap comprehensively covers various areas of AI engineering. Each section can be learned independently, and you can adjust the learning order according to your interests and goals.

Recommended Learning Order

1. Coding & ML Fundamentals: First learn programming and machine learning concepts that form the foundation of AI engineering.
2. LLM APIs: Learn basic usage of large language models.
3. Model Adaptation: Learn how to adjust models through prompt engineering and fine-tuning.
4. Storage & RAG: Learn how to build data storage and retrieval systems.
5. AI Agents: Learn how to design and implement autonomous AI agents.
6. Infrastructure & Security: Consider deployment and security in production environments.

Learning Tips

• Try implementing the practical examples from each section yourself.
• Participate in relevant communities to share experiences with other developers.
• Continuously update with the latest trends and technologies.

We hope this roadmap helps you start your journey in AI engineering. Please proceed with learning at your own pace and according to your interests.

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