Full-stack AI education
Learn Full-Stack AI by Building Real Systems
Master the software, data, models, agents and infrastructure behind production-ready AI products—from first principles to deployment.
Structured learning paths · Real projects · Interactive tools · Production architecture
The full-stack AI system
User Experience
Frontend
Application Layer
APIs · Auth · Logic
Agents and Workflows
Tools · State · Memory
Models and Retrieval
LLMs · RAG · Evaluation
Data and Knowledge
SQL · Vectors · Pipelines
Infrastructure
Cloud · CI/CD · Observability
Software
→
Data
→
Models
→
Agents
→
Production
Structured routes
Choose Your Learning Path
Start at your current level and move toward a complete, deployable system.
Beginner · 8–12 weeks
Full-Stack AI Foundations
Python, Git, APIs, SQL, frontend, backend and deployment fundamentals.
Intermediate · 6–8 weeks
Build LLM Applications
Prompting, structured outputs, embeddings, RAG, evaluation and deployment.
Intermediate · 6–10 weeks
AI Agent Engineer
Tools, state, memory, workflows, orchestration and human approval systems.
Advanced · 8–12 weeks
Production AI Engineer
Containers, cloud, CI/CD, observability, security, scaling and cost control.
Featured project
Build a Production RAG Application
Create an end-to-end system with document ingestion, chunking, embeddings, vector search, reranking, evaluation, an API layer and deployment.
- Frontend interface and FastAPI backend
- PostgreSQL and vector retrieval
- Evaluation, tracing and monitoring
- Docker-based deployment
System flow
User Interface
↓
FastAPI Application
↓
PostgreSQL
Vector Search
LLM
↓
Evaluation · Tracing · Deployment
Learn by experimenting
Interactive Labs
Explore important concepts through immediate feedback and editable controls.
Cost and Tokens
LLM Cost Calculator
Estimate model cost from token volume, request frequency and workload assumptions.
Retrieval
Chunking Playground
Compare chunk size, overlap and retrieval behavior on sample documents.
Agents
Workflow Visualizer
Map tools, decisions, state and human approval across an agent workflow.
Explore the platform
Eight Knowledge Areas
Navigate the complete technical stack without losing the connection between layers.
Foundations
Programming, math, Git, Linux and computer science.
Application Development
Frontend, backend, APIs, authentication and testing.
Data Systems
SQL, pipelines, warehouses, streaming and vectors.
Machine Learning
Training, validation, deep learning, NLP and vision.
LLM Engineering
Prompting, RAG, multimodal systems and evaluation.
Agentic Systems
Tools, workflows, memory, state and orchestration.
Production Infrastructure
Cloud, CI/CD, observability, security and scaling.
Product and Research
Product strategy, papers, repositories and careers.
New on the platform
Latest from FS AI Hub
New tutorials, project builds, research breakdowns and production lessons.
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Mastering Full-Stack AI: A Comprehensive Guide
Introduction to Full-Stack AI In today’s rapidly evolving landscape of technology, the demand for professionals skilled in full-stack AI is greater than ever. This…
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Mastering Full-Stack AI: A Comprehensive Approach
Understanding Full-Stack AI Development Full-stack AI development encompasses the complete lifecycle of artificial intelligence systems, integrating various disciplines such as programming, database management, and…
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