AI & ML Engineering Academy
LLMs · RAG · Agents · MLOps
Build production AI applications — from LLM fundamentals to RAG pipelines, AI agents, and LLMOps. Practical, engineering-focused, not just theory.
👤 Who This Is For
📋 Prerequisites
🎯 What You'll Be Able to Do
🗺️ Recommended Learning Path
📚 Explore Topics
AI Foundations
Core concepts every AI engineer must know
ML concepts, neural networks, model types — from zero to AI-ready
Python for AI/ML work — data types, functions, OOP, working with JSON/APIs, intro to numpy. Not infra scripting.
Zero-shot, few-shot, CoT, ReAct, system prompts — master LLM communication
LLM Application Development
Build production LLM-powered applications
The discipline of building production LLM applications — model selection, context management, evaluation, cost control
LLM framework — chains, LCEL, memory, tools, structured output
Retrieval Augmented Generation — chunking, embeddings, vector DBs, evaluation
Autonomous agents — ReAct, tool use, multi-agent, memory systems, production
MLOps & Production AI
Deploy, monitor, and maintain AI systems