AI Product Manager Master Course

$500.00

Category:

Description

What is an AI Product Manager?

An AI Product Manager is a professional who guides the strategy, development, and lifecycle of products that use artificial intelligence technologies. This role blends traditional product management skills with deep understanding of AI capabilities, data constraints, ethical use, and cross‑functional collaboration between business, design, and technical teams. AI Product Managers help translate complex AI opportunities into real, scalable, user‑centered products that create business value.

 

Top Industries Hiring AI Product Managers

  1. Technology & SaaS (Software)
  • Big tech and cloud/SaaS platforms lead the demand.
  • AI PMs work on intelligent services, recommendation engines, ML platforms, and enterprise tools.
  1. Banking, Finance & FinTech
  • Roles focus on AI solutions for fraud detection, risk scoring, automated investing, and personalized financial products.
  1. Healthcare & Life Sciences
  • Companies use AI for diagnostics, medical imaging analysis, predictive healthcare analytics, drug discovery, and patient engagement solutions.
  1. E‑Commerce & Retail
  • AI PMs help build systems for personalized recommendations, dynamic pricing, inventory forecasting, and optimized shopping experiences.
  1. Automotive & Mobility
  • Growing need in autonomous driving tech, connected vehicles, and smart transportation systems.
  1. Manufacturing & Industry 4.0
  • AI-powered quality control, predictive maintenance, and automated operations create opportunities for product leadership.
  1. Media, Entertainment & Advertising
  • Roles focus on AI for content recommendation, audience insights, dynamic ad targeting, and media personalization.
  1. Cybersecurity
  • With AI‑driven threat detection and risk response tools, this sector is increasingly hiring specialized AI PMs.
  1. Education & EdTech
  • AI Product Managers build adaptive learning platforms, automated grading systems, and intelligent tutoring applications.

 

Job Market for AI Product Managers

Demand & Growth

  • The demand for AI Product Managers continues to grow quickly as more companies build AI‑driven products and services. Roles that require both product leadership and AI/ML understanding are outpacing traditional product manager openings.
  • Reports show that AI‑related roles (including AI PM) have doubled in demand in the past year, with thousands of professionals moving into these jobs.
  • Growth is strongest in tech hubs like San Francisco, New York, Seattle, but remote opportunities are increasing as well.

 

Market Outlook

  • Adoption of AI across industries (tech, healthcare, finance, retail, automotive, etc.) is fueling long‑term demand for product leaders with AI experience.
  • Companies increasingly look for PMs who can own AI strategy, understand models, evaluate data quality, and manage ethical risks — not just traditional product planning.

 

Expected Salary for AI Product Managers

Salaries can vary widely by experience, company size, industry, and location – especially bigger tech companies and well‑funded AI startups. Here’s a breakdown:

Typical Salary Ranges (U.S.)

Entry‑Level (0–2 years)

  • Usually: ~$85,000 – $110,000 per year

Mid‑Level (3–5 years)

  • Typically: ~$110,000 – $150,000+ per year

Senior AI Product Manager

  • Around: ~$150,000 – $200,000+ per year

Manager/Director Level

  • Can reach $170,000 – $200,000+ and above

The following topics will be covered as part of AI Product Manager Master Course.

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

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

Prerequisites :- 

  • Basic understanding of software products & Product Management
  • Familiarity with APIs and web applications
  • Basic understanding of data and analytics

Duration & Timings :

Total Hours – 40  Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Access to Class Recordings.

Schedule:

Weekday Session – Mon – Thu 8:30 PM to 10:30 PM (EST) – 5 Weeks. April 6, 2026.

Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 7 Weeks. Saturday, May 2, 2026. 

 Inquiry Now 

USA: +1 734 418 2465 | India: +91 7416031568

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