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🚀Full-Stack AIIntermediate

Full-Stack AI Engineer Path

Become a versatile AI engineer who can build end-to-end AI-powered applications. From frontend to backend to ML models - own the entire stack. Perfect for startups and product teams.

18 weeks
9 milestones
0 items

Skills You Will Gain

React/Next.jsPython BackendLLM IntegrationRAG SystemsVector DatabasesAI APIsFull-Stack DeploymentProduct Thinking

Prerequisites

  • →Web development basics (HTML, CSS, JavaScript)
  • →Python programming
  • →Basic understanding of databases
  • →Familiarity with APIs

Learning Milestones

1

Modern Frontend Development

Build polished AI-powered user interfaces with React and Next.js.

~22h•0 items

Learning Objectives

  • ✓Master React fundamentals and hooks
  • ✓Build with Next.js App Router
  • ✓Create responsive, accessible UIs with Tailwind
  • ✓Handle streaming responses in UI
  • ✓Implement real-time updates
  • ✓Build chat interfaces and AI interactions
Content coming soon
2

Python Backend Development

Build robust backend services for AI applications.

Content Summary

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Concepts
0
Papers
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Lectures
0
Problems
~20h•0 items

Learning Objectives

  • ✓Build APIs with FastAPI
  • ✓Handle async operations for AI workloads
  • ✓Implement authentication and authorization
  • ✓Design scalable API architectures
  • ✓Handle file uploads and processing
  • ✓Build background job systems
Content coming soon
3

Database Design for AI Apps

Design and implement databases optimized for AI applications.

~15h•0 items

Learning Objectives

  • ✓Design schemas for AI applications
  • ✓Use PostgreSQL effectively (including pgvector)
  • ✓Implement caching with Redis
  • ✓Handle vector data alongside relational data
  • ✓Optimize database performance
  • ✓Handle data migrations
Content coming soon
4

LLM Integration Patterns

Master patterns for integrating LLMs into applications.

~18h•0 items

Learning Objectives

  • ✓Integrate OpenAI, Anthropic, and open-source LLMs
  • ✓Implement streaming responses
  • ✓Handle errors and rate limits gracefully
  • ✓Build conversation management systems
  • ✓Implement prompt templates and versioning
  • ✓Optimize for cost and latency
Content coming soon
5

Building RAG Applications

Build production-ready RAG systems end-to-end.

~22h•0 items

Learning Objectives

  • ✓Design document processing pipelines
  • ✓Implement vector search with multiple backends
  • ✓Build retrieval and generation pipelines
  • ✓Create user-friendly document upload UIs
  • ✓Handle multi-format documents
  • ✓Implement source attribution and citations
Content coming soon
6

AI Agent Integration

Build applications that leverage AI agents for complex tasks.

~18h•0 items

Learning Objectives

  • ✓Integrate LangChain agents into apps
  • ✓Build tool-using agents with UI feedback
  • ✓Handle agent errors gracefully
  • ✓Create agent monitoring dashboards
  • ✓Implement human-in-the-loop workflows
  • ✓Design agent-powered features
Content coming soon
7

Authentication & Multi-tenancy

Implement secure authentication and multi-tenant AI apps.

~15h•0 items

Learning Objectives

  • ✓Implement OAuth and JWT authentication
  • ✓Design multi-tenant architectures
  • ✓Handle API key management
  • ✓Implement rate limiting per user
  • ✓Build subscription and billing systems
  • ✓Handle data isolation
Content coming soon
8

Deployment & DevOps

Deploy and operate AI applications in production.

~18h•0 items

Learning Objectives

  • ✓Deploy with Vercel, Railway, or AWS
  • ✓Containerize applications with Docker
  • ✓Set up CI/CD pipelines
  • ✓Implement monitoring and logging
  • ✓Handle scaling and load balancing
  • ✓Manage environment variables and secrets
Content coming soon
9

Building AI Products

Apply product thinking to AI application development.

~15h•0 items

Learning Objectives

  • ✓Identify AI-native product opportunities
  • ✓Design AI-first user experiences
  • ✓Handle AI uncertainty in UX
  • ✓Collect and use user feedback
  • ✓Measure AI feature success
  • ✓Iterate based on real usage
Content coming soon