AI Product Management

Build AI Products that Matter—From Vision to Execution

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

AI Product Management is a comprehensive, industry-oriented course tailored for aspiring product managers, entrepreneurs, and technologists. The program equips learners with end-to-end knowledge of launching AI-driven products—covering everything from problem discovery, data sourcing, model selection, and user experience to team management, deployment, and continuous improvement. It bridges the gap between technical teams and business outcomes in the evolving AI product landscape.

Aim: To train professionals in the essential skills required to manage AI product development from initial concept to successful launch, blending product strategy, data understanding, ethical AI deployment, and cross-functional leadership.

Program Objectives:

To instill a product-first mindset for AI innovations
To bridge business goals with machine learning capabilities
To equip professionals to manage uncertainty and complexity in AI
To emphasize ethical and explainable AI as core product values

What you will learn?

Week 1: Foundations of AI and Product Thinking

Module 1: Introduction to AI Product Management

Chapter 1.1: Role of the AI Product Manager
Chapter 1.2: How AI Products Differ from Traditional Software
Chapter 1.3: AI Product Lifecycle Overview

Module 2: Fundamentals of AI & Machine Learning

Chapter 2.1: AI/ML Basics for Product Managers
Chapter 2.2: Types of Models (Supervised, Unsupervised, Generative)
Chapter 2.3: Data Lifecycle and Its Impact on Products
Chapter 2.4: Understanding Evaluation Metrics and Trade-offs

Week 2: Designing and Building AI Products

Module 3: From Use Case to Model

Chapter 3.1: Identifying the Right AI Use Cases
Chapter 3.2: Scoping MVPs for AI Capabilities
Chapter 3.3: Working with Data, Labels, and Annotation
Chapter 3.4: Model Selection and Collaboration with Data Science

Module 4: AI Product Design and UX

Chapter 4.1: Designing for Explainability and Trust
Chapter 4.2: Human-in-the-Loop Design Patterns
Chapter 4.3: Feedback Loops and Active Learning
Chapter 4.4: Ethical Considerations in AI Interfaces

Week 3: Deployment, Scaling, and Strategy

Module 5: AI Delivery and Product Operations

Chapter 5.1: Model Deployment Workflows and Tools
Chapter 5.2: A/B Testing and Monitoring in AI Systems
Chapter 5.3: Managing Drift, Retraining, and Model Updates
Chapter 5.4: Platform Choices and MLOps Integration

Module 6: Strategy, Roadmapping, and Stakeholder Management

Chapter 6.1: Building an AI Product Strategy
Chapter 6.2: Creating Roadmaps for Data-Driven Products
Chapter 6.3: Managing Cross-Functional Teams in AI
Chapter 6.4: Navigating Legal, Compliance, and Risk in AI

Intended For :

Product managers, business analysts, and project leaders
Founders, entrepreneurs, and startup teams
Engineers and designers transitioning into AI product roles
No coding required, but technical curiosity is encouraged