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AI Product Management

Course Title:

AI Product Management: Hands-On Approach with Real-World Use Cases

Course Duration:

3 months (12 weeks)

Frequency:

2 classes per week

Audience:

Product managers, AI enthusiasts, and professionals transitioning into AI product management roles.

Course Objective:

Equip learners with both theoretical knowledge and hands-on experience to successfully manage AI-driven products, from ideation to deployment. Students will develop and work on real-world AI product cases and use leading AI tools.

Course Content

Week 1: Introduction to AI Product Management

  • Lecture 1 : Role of AI in Product Management
    • Overview: What makes AI product management different?
    • Use Case: AI product strategy for personalization (e.g., Netflix recommendation system).
  • Lecture 2 : AI Product Lifecycle Overview
    • Key Topics: Stages from ideation to model deployment, customer feedback integration.
    • Exercise: Create a lifecycle roadmap for an AI-based product (e.g., AI chatbot).
  • Tools Required:
    • Trello or JIRA (for roadmap planning)
    • Miro (for product lifecycle mapping)

Week 2: AI Fundamentals for Product Managers

  • Lecture 1: Introduction to AI & ML Concepts
    • Key Topics: Basic AI/ML concepts—supervised, unsupervised, reinforcement learning.
    • Use Case: AI for fraud detection in fintech.
  • Lecture 2: Understanding ML Pipelines
    • Key Topics: Data collection, model training, evaluation, deployment.
    • Exercise: Define an ML pipeline for a sample AI product (e.g., image classification).
  • Tools Required:
    • Google Colab (for hands-on practice)
    • Python (for basic model training with Scikit-learn)

Week 3: Product Discovery and Market Fit

  • Lecture 1: Identifying Market Opportunities for AI
    • Key Topics: Analyzing where AI adds business value.
    • Use Case: Personalization with AI (e.g., Spotify’s recommendation algorithm).
  • Lecture 2: Defining Product Features Based on AI Capabilities
    • Exercise: Identify core AI product features for a customer support automation tool.
  • Tools Required:
    • Google Sheets for feature prioritization
    • Figma for wireframing product features

Week 4: Data Strategy & Management in AI Products

  • Lecture 1: Data Collection, Cleaning, and Labeling
    • Key Topics: Importance of high-quality data for AI products.
    • Use Case: Data pipeline for a smart healthcare monitoring system.
  • Lecture 2: Ethical AI and Data Privacy
    • Key Topics: Addressing bias, fairness, and privacy regulations like GDPR.
    • Exercise: Design a privacy policy for an AI-driven fintech product.
  • Tools Required:
    • Python Pandas (for data cleaning tasks)
    • Labelbox (for labeling and managing datasets)

Week 5: AI Product Roadmapping

  • Lecture 1: Planning AI Product Roadmaps
    • Key Topics: Defining short-term vs long-term AI product goals.
    • Use Case: Roadmap for AI-powered e-commerce search optimization.
  • Lecture 2: Prioritizing AI Features
    • Exercise: Create a roadmap with prioritized features for an AI-driven voice assistant.
  • Tools Required:
    • ProductPlan (for roadmap planning)
    • Google Sheets (for feature matrix prioritization)

Week 6: Cross-Functional Team Management

  • Lecture 1: Working with AI/ML Engineers
    • Key Topics: How to communicate and align with technical teams.
    • Use Case: Managing AI teams in a collaborative project for a recommendation engine.
  • Lecture 2: Stakeholder Communication
    • Exercise: Conduct a mock meeting to align business needs with AI product development.
  • Tools Required:
    • Slack/Zoom (for team collaboration)
    • Miro (for interactive meetings and workshops)

Week 7: Model Evaluation and Metrics

  • Lecture 1: Understanding AI Model Metrics
    • Key Topics: Accuracy, precision, recall, F1-score, and their relevance to AI products.
    • Use Case: Evaluating an AI model for autonomous driving systems.
  • Lecture 2: Fine-tuning Models for Performance
    • Exercise: Tune an AI model to improve its accuracy (e.g., image classification model).
  • Tools Required:
    • TensorFlow or PyTorch (for model training)
    • Google Colab (for hands-on evaluation)

Week 8: AI Deployment & MLOps

  • Lecture 1: Introduction to MLOps for AI Products
    • Key Topics: Continuous deployment, versioning, retraining AI models.
    • Use Case: MLOps pipeline for a predictive maintenance system in manufacturing.
  • Lecture 2: AI Product Deployment at Scale
    • Exercise: Deploy a trained AI model to AWS or Google Cloud and track performance.
  • Tools Required:
    • AWS SageMaker / Google Cloud AI
    • GitHub for version control

Week 9: Go-to-Market Strategy for AI Products

  • Lecture 1: Monetizing AI Products
    • Key Topics: Pricing models, customer acquisition, and distribution strategies for AI products.
    • Use Case: Go-to-market strategy for an AI-powered SaaS platform.
  • Lecture 2: Scaling AI Solutions
    • Exercise: Draft a go-to-market strategy for an AI product (e.g., voice recognition app).
  • Tools Required:
    • Google Analytics (for tracking metrics)
    • Canva (for marketing visuals)

Week 10: Post-Launch Monitoring & Continuous Improvement

  • Lecture 1: Monitoring AI Systems
    • Key Topics: Detecting model drift, handling user feedback, and improving product post-launch.
    • Use Case: Monitoring AI for financial portfolio management systems.
  • Lecture 2: Retraining and Maintaining AI Models
    • Exercise: Create a retraining and monitoring strategy for a real-time AI system.
  • Tools Required:
    • MLflow (for tracking model performance and retraining)
    • Grafana (for real-time monitoring)

Week 11: Ethics and Future Trends in AI

  • Lecture 1: Explainable AI (XAI)
    • Key Topics: Making AI models transparent and understandable for stakeholders.
    • Use Case: XAI for healthcare diagnostics.
  • Lecture 2: AI Ethics and Legal Considerations
    • Exercise: Create an ethical guideline for a healthcare AI product.
  • Tools Required:
    • LIME or SHAP (for model explainability)

Week 12: Capstone Project and Final Review

  • Lecture 1 & 2: Capstone Project
    • Project: Design an AI product strategy from data collection to deployment. Develop the complete roadmap, identify market fit, and define go-to-market strategies.
    • Presentation: Each participant will present their AI product roadmap and strategy for peer review.
  • Tools Required:
    • All previously introduced tools (JIRA, Figma, TensorFlow, etc.)