Module 1: Foundations of AI Product Management
- What is AI product management?
- Differences between traditional PM and AI PM
- Core AI concepts: machine learning, deep learning, NLP, computer vision
- Lifecycle of AI products: ideation → data → model → deployment → monitoring
Module 2: Understanding AI Capabilities
- Identifying problems AI can solve vs. those it cannot
- Evaluating feasibility: data availability, model maturity, compute resources
- AI product categories: predictive, generative, autonomous, assistive
- Case studies: recommendation systems, chatbots, fraud detection
Module 3: Data Strategy
- Importance of data in AI products
- Data collection, labeling, and governance
- Building data pipelines and ensuring quality
- Privacy and compliance (GDPR, CCPA, NDPR)
Module 4: Model Development and Evaluation
- Collaborating with data scientists and ML engineers
- Understanding model training, validation, and deployment
- Key metrics: accuracy, precision, recall, F1-score, AUC
- Trade-offs: performance vs. interpretability
Module 5: AI Product Design
- Designing user experiences around AI outputs
- Human-in-the-loop systems
- Explainability and transparency in product design
- Case study: designing an AI-powered healthcare assistant
Module 6: MLOps and Deployment
- Model serving and APIs
- CI/CD pipelines for AI products
- Monitoring drift and retraining models
- Tools: MLflow, Kubeflow, TensorFlow Serving
Module 7: Business and Market Strategy
- Positioning AI products in the market
- Pricing strategies for AI-driven solutions
- Competitive analysis and differentiation
- Measuring ROI and product success
Module 8: Ethics and Responsible AI
- Bias detection and mitigation
- Fairness and inclusivity in AI products
- Regulatory frameworks and compliance
- Building trust with stakeholders
Module 9: Future Trends
- Generative AI products (LLMs, diffusion models)
- AI agents for autonomous workflows
- AI in edge computing and IoT
- AI + blockchain for secure product ecosystems