AI Product Development & Deployment
From concept to production: Build and ship AI applications that scale
Who Should Take This Course
Product managers, full-stack developers, AI engineers, startup founders, and technical leaders who want to ship production-ready AI applications.
Prerequisites
Basic programming experience (e.g., Python), understanding of web applications and APIs, familiarity with databases. Some exposure to AI concepts helpful but not required.
Comprehensive Curriculum
14 key topics covering the complete AI product development lifecycle
AI Product Strategy and Feature Design
Model Selection and Evaluation for Production
Prompt Engineering Patterns and Best Practices
RAG (Retrieval-Augmented Generation) Systems
Vector Databases and Semantic Search
API Design and Integration Strategies
Fine-tuning vs Prompt Engineering Trade-offs
Deployment Architectures and Infrastructure
Monitoring, Logging, and Observability
A/B Testing and Experimentation
Cost Optimization and Scaling
Security, Privacy, and Compliance
Ethics and Responsible AI Deployment
User Feedback Loops and Iteration
What You'll Achieve
Real-World Projects
Project 1: RAG-Powered Q&A System
Build a complete question-answering system using RAG. Implement document ingestion, chunking, embedding, retrieval, and response generation. Deploy to production.
Project 2: Semantic Search Application
Create a semantic search engine with vector databases. Implement hybrid search combining keyword and semantic search for optimal results.
Project 3: AI-Powered Chatbot with Memory
Build an intelligent chatbot with conversation memory, tool use capabilities, and personalization. Handle multi-turn conversations effectively.
Final Project: Complete AI Application
Design and build a full-stack AI application from ideation to deployment. Include monitoring, cost optimization, and user feedback loops. Present your product.
Ready to Build Production AI Products?
Join our most comprehensive course and learn to ship AI applications that make an impact.