40-Hour Professional Training Program
Target Audience
• Product Managers
• Business Analysts
• Software Engineers
• Data Analysts
• Startup founders
Prerequisites
• Basic understanding of software products
• Familiarity with APIs and web applications
• Basic understanding of data and analytics
Course Duration: 40 Hours
| Component | Hours |
|---|---|
| Concepts | 18 |
| Hands-on Labs | 12 |
| Case Studies | 5 |
| Capstone Project | 5 |
MODULE 1 — AI Product Management Foundations
Objective
Understand the fundamentals of AI-driven products.
Topics
• Evolution of AI products
• AI vs traditional software products
• AI product lifecycle
• AI capabilities in modern products
AI Product Lifecycle
Problem → Data → Model → Product → Deployment → Monitoring
AI Product Examples
• ChatGPT
• Netflix recommendation engine
• Amazon product recommendation system
• Google Assistant
Exercise
Analyze 3 AI products and identify:
• AI capability
• Data used
• Business value
MODULE 2 — Role of an AI Product Manager
Responsibilities
• AI strategy definition
• Problem framing
• AI opportunity identification
• Managing AI lifecycle
• Working with ML teams
AI Product Manager vs Traditional PM
| Traditional PM | AI PM |
|---|---|
| Feature driven | Data driven |
| Deterministic | Probabilistic |
| Engineering focused | Data + Model focused |
Stakeholders
• Data Scientists
• ML Engineers
• Data Engineers
• UX Designers
• Business teams
Exercise
Define the AI product strategy for fraud detection.
MODULE 3 — AI & Machine Learning Concepts for PMs
(No coding required)
AI Fundamentals
Machine Learning
Deep Learning
Natural Language Processing
Computer Vision
Generative AI
ML Types
Supervised learning
Unsupervised learning
Reinforcement learning
Key Algorithms
Linear Regression
Logistic Regression
Decision Trees
Random Forest
Neural Networks
Evaluation Metrics
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Demo
Prediction model using Python
Participants observe:
• training data
• model predictions
• evaluation metrics
MODULE 4 — Identifying AI Opportunities
⏱ Duration: 4 Hours
AI Opportunity Framework
1 Problem definition
2 Data availability
3 AI feasibility
4 Business value
5 Implementation complexity
Industry AI Use Cases
Retail
Healthcare
Finance
Manufacturing
Marketing
Case Study
Customer churn prediction system.
Workshop
Participants design AI use cases for their organization.
MODULE 5 — Data Strategy for AI Products
Topics
• Data collection
• Data labeling
• Data pipelines
• Feature engineering
• Data governance
Data Challenges
Bias
Data drift
Incomplete data
Data quality
Exercise
Design a data pipeline for a recommendation system.
MODULE 6 — AWS AI & ML Ecosystem
AWS AI Architecture
Data Layer → ML Layer → Application Layer
Key AWS Services
• Amazon Web Services
• Amazon S3
• AWS Lambda
• Amazon API Gateway
MODULE 7 — Generative AI Products with AWS
Topics
Large Language Models
Prompt engineering
RAG architecture
Fine-tuning concepts
AWS Generative AI Services
• Amazon Bedrock
• Amazon SageMaker
MODULE 8 — Designing AI Products
Topics
Human-AI interaction
Explainable AI
Designing for uncertainty
Tools
• Figma
• Miro
Exercise
Design the UX flow for an AI support assistant.
MODULE 9 — AI Product Development Lifecycle
AI Lifecycle
Problem definition
Data preparation
Model development
Evaluation
Deployment
Monitoring
MLOps Concepts
Continuous training
Model monitoring
Data drift detection
MODULE 10 — AI Product Metrics
Metrics
Model accuracy
Latency
User adoption
Engagement rate
Retention
Experimentation
A/B testing
Online experiments
Feedback loops
Exercise
Create an AI product KPI framework.
MODULE 11 — AI Ethics & Governance
Topics
Bias and fairness
Responsible AI
Explainability
Data privacy
Discussion
Should AI decisions be transparent?
MODULE 12 — Capstone Project
Participants design a complete AI product concept.
Deliverables
Problem statement
AI architecture using AWS
Data strategy
Product roadmap
Monetization model
Pitch presentation
Example Projects
- AI Resume Analyzer
- AI Customer Support Bot
- AI Fraud Detection System
- AI Recommendation Engine
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