Applied AI for Business

Course Description

Take your AI skills from theory to production. This intermediate course teaches you how to build, deploy, and manage AI-powered applications for real business use cases. You'll work with APIs, build custom chatbots, create AI pipelines, and learn the fundamentals of AI system architecture.

Some technical background is helpful but not mandatory — we'll teach you what you need.

Who Is This For?

• Graduates of Introduction to Generative AI (or equivalent knowledge)
• Business analysts and product managers building AI features
• Entrepreneurs building AI-powered products or services
• Technical professionals transitioning into AI roles

Prerequisites

• Completion of "Introduction to Generative AI" or equivalent experience
• Basic familiarity with using AI tools (ChatGPT, Claude)
• Willingness to learn basic coding concepts (Python)
• Requires application and approval

Syllabus

Module 1: The AI Application Stack

• From prompts to products: the full stack
• APIs, SDKs, and integration patterns
• Cloud services for AI (AWS, GCP, Azure overview)
• Setting up your development environment

Module 2: Working with AI APIs

• OpenAI and Anthropic API basics
• Authentication, rate limits, and cost management
• Building your first API-powered application
• Error handling and retry strategies

Module 3: Building Custom Chatbots

• Conversational AI design principles
• RAG (Retrieval-Augmented Generation) fundamentals
• Building a knowledge-base chatbot
• Deploying chatbots to messaging platforms

Module 4: AI Pipelines & Automation

• Designing multi-step AI workflows
• Document processing pipelines
• Automated content generation systems
• Monitoring and observability

Module 5: Data & Vector Databases

• Embeddings and semantic search
• Vector database setup (Pinecone, Weaviate)
• Building a semantic search application
• Data preparation and chunking strategies

Module 6: AI System Design

• Architecture patterns for AI applications
• Scaling considerations and cost optimization
• Security and privacy in AI systems
• The AI product lifecycle

Module 7: Deployment & Operations

• Deploying AI applications to production
• Monitoring, logging, and alerting
• A/B testing AI features
• Iterating based on user feedback

Module 8: Capstone Project

• Build a production-ready AI application
• Demo day with industry judges
• Portfolio piece for your career
• Peer feedback and networking

What You'll Get

• 36 hours of instructor-led training (12 sessions x 3 hours)
• Access to cloud computing credits (AWS/GCP)
• Project starter templates and boilerplate code
• 1-on-1 office hours with instructor
• Certificate of completion
• SkillsFuture Credit eligible (pending SSG accreditation)