Builder Track

AI Development for Developers

Build production-ready AI applications from scratch with hands-on projects

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Total Duration

48-60 hours

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Course Modules

6 modules

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Learning Style

Hands-on coding

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Production Labs

6 projects

🚀 What You'll Build

This track takes you from Python basics to production AI applications:

Python Fundamentals for AI - Modern Python 3.12+ features, essential libraries, async programming for AI APIs
Working with APIs - Master REST APIs, LLM integrations (OpenAI, Anthropic, Google), rate limiting, streaming
Building AI Applications - AI app architectures, frontend/backend frameworks, AI SDKs, chat interfaces
Vector Databases & RAG - Build RAG applications with Pinecone, Weaviate, Qdrant, ChromaDB, pgvector
AI Agents Development - Create intelligent agents with LangChain, LangGraph, CrewAI, Phidata
Deployment & Production - Deploy with Docker, Vercel, Railway, Fly.io, implement monitoring and CI/CD

⚡ Prerequisites

This track is for developers who want to build AI applications. You should have:

Basic programming experience (1+ year in any language)
Willingness to learn Python (we start from fundamentals)
Familiarity with terminal/command line
Basic understanding of APIs and web development

New to coding? Consider starting with Python tutorials first. Already advanced? Check out our Deep Dive Track for production systems and optimization.

Course Modules

Module 1

🎁 Free Preview

Python Fundamentals for AI

Master modern Python 3.12+ features essential for AI development. Learn essential libraries (NumPy, Pandas, Requests), work with JSON and data structures, manage virtual environments with uv and poetry, and understand async programming for AI API calls. Build a solid foundation for AI application development. Start here—completely free.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Python for AI Toolkit ✅ Interactive exercises

Start Free Module →

Module 2

🔒 Premium

Working with APIs

Learn REST API fundamentals and integrate LLM APIs from OpenAI, Anthropic, Google, Mistral, and Groq. Master authentication patterns, rate limiting, error handling, and streaming responses. Build a unified AI API client that works with multiple providers.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Multi-Provider AI Client 💼 Production patterns

Module 3

🔒 Premium

Building AI Applications

Design and build complete AI applications with modern frameworks. Learn Next.js 14, SvelteKit, Gradio, and Streamlit for frontends. Master FastAPI and Flask for backends. Work with Vercel AI SDK, LangChain, and LlamaIndex. Build production-ready chat interfaces and AI-powered web apps.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Full-Stack AI Chat App 🚀 Deployment ready

Module 4

🔒 Premium

Vector Databases & RAG

Master Retrieval-Augmented Generation (RAG) to build AI apps that can answer questions about your own data. Learn vector databases (Pinecone, Weaviate, Qdrant, ChromaDB, pgvector), build RAG applications with LangChain and LlamaIndex, and implement advanced RAG techniques like hybrid search and re-ranking.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Document Q&A RAG System 🔍 Advanced techniques

Module 5

🔒 Premium

AI Agents Development

Build intelligent AI agents that can use tools, maintain memory, and solve complex problems autonomously. Master the ReAct pattern, work with LangChain, LangGraph, CrewAI, and Phidata. Implement tool integration, function calling, multi-agent systems, and agent memory/state management.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Research Assistant Agent 🤖 Multi-agent systems

Module 6

🔒 Premium

Deployment & Production

Deploy your AI applications to production with modern infrastructure. Master Docker containerization, deploy to Vercel, Railway, Fly.io, and Render. Learn serverless AI with AWS Lambda, Vercel Edge, and Cloudflare Workers AI. Implement monitoring, logging, cost optimization, and CI/CD pipelines.

📚 5 chapters ⏱️ ~8-10 hours 🔬 Lab: Production AI App Deployment ☁️ Cloud deployment

🔬 Hands-On Labs

Every module includes a production-grade lab where you build real applications:

Module 1: Build a Python toolkit for AI with async API calls and data processing
Module 2: Create a multi-provider AI client that works with OpenAI, Anthropic, Google, Mistral, and Groq
Module 3: Build a full-stack AI chat application with Next.js and Vercel AI SDK
Module 4: Create a document Q&A system using RAG with Pinecone and LangChain
Module 5: Deploy a research assistant agent that can search the web and write reports
Module 6: Deploy a production AI application with Docker, Railway, monitoring, and CI/CD

Total lab time: 24-30 hours | Real code, real deployments, real portfolio projects.

🛠️ Technologies & Tools (2025 Trending)

You'll work with the latest AI development tools and frameworks:

Python Tools

uv (package manager), ruff (linter/formatter), pyright (type checker), NumPy, Pandas, Requests, asyncio

LLM APIs & SDKs

OpenAI, Anthropic (Claude), Google (Gemini), Mistral, Groq, Vercel AI SDK, LangChain, LlamaIndex

Frontend Frameworks

Next.js 14, SvelteKit, Gradio, Streamlit, React, Tailwind CSS

Backend Frameworks

FastAPI, Flask, Node.js, Express

Vector Databases

pgvector (hot 2025!), Pinecone, Weaviate, Qdrant, ChromaDB

Agent Frameworks

Phidata, LangGraph, CrewAI, LangChain Agents

Deployment & Infrastructure

Docker, Railway (hot 2025!), Vercel, Fly.io, Render, AWS Lambda, Cloudflare Workers AI, Modal

AI-Powered Dev Tools

Cursor IDE, v0.dev, Lovable.dev, Bolt.new

💰 Cost Breakdown

Estimated API and hosting costs for completing all labs:

Module 1: Free (local Python development)
Module 2: $2-5 (LLM API calls, most providers offer free credits)
Module 3: $3-7 (Next.js + AI SDK deployment on Vercel free tier)
Module 4: $5-10 (Pinecone free tier + OpenAI embeddings)
Module 5: $3-8 (Agent tool calls and web search APIs)
Module 6: $5-15 (Railway/Fly.io hosting, some free tiers available)

Total range: $18-45 | Most services offer generous free credits and tiers for learning.

📈 Career Impact

Completing this track positions you for AI development roles:

AI Application Developer: $90K-$150K+ (Mid level)
Full-Stack AI Engineer: $100K-$170K+ (Full-stack with AI focus)
AI/ML Engineer: $110K-$180K+ (Engineering focus)
LLM Integration Engineer: $120K-$190K+ (Specialized role)
Senior AI Developer: $130K-$200K+ (Senior level)

Skills most in demand: RAG systems, AI agents, production deployment, and modern frameworks. This track covers all of them with hands-on projects you can show employers.

🎁 What You'll Have After Completion

6 Portfolio Projects: Production-ready AI applications to showcase
Complete GitHub Repository: All code, labs, and projects
Deployment Experience: Apps running live on the web
API Integration Skills: Work with any LLM provider
RAG System Expertise: Build AI apps with custom knowledge
Agent Development Skills: Create autonomous AI assistants
Production Knowledge: Deploy, monitor, and scale AI apps

Ready to Build AI Applications?

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6 production labs

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