This module covers the end-to-end lifecycle of AI model development and maintenance, including model selection, training, deployment, testing, and ethical refresh practices. Learners will gain insights into how to manage AI products throughout their lifecycle and ensure continuous performance and reliability.

This module provides an in-depth exploration of machine learning and deep learning, covering their differences, applications, and the importance of model explainability. It also delves into the historical context, neural network types, and strategies for optimizing AI performance. Learners will gain a clear understanding of when and how to apply these technologies effectively.

This module explores strategies for commercializing AI products, including B2B and B2C models, blue ocean and red ocean markets, and examples of successful AI product strategies. Learners will gain insights into how to differentiate AI products and develop competitive market strategies.

This module explores how AI is reshaping various sectors including economics, healthcare, government, and business. It emphasizes ethical considerations and practical applications for product managers, offering insights into AI's transformative potential and challenges.

This module provides a comprehensive overview of building AI-native products, covering strategic decision-making, team roles, data management, and tech stack investments. Learners will gain insights into the key components of AI product development and how to structure a successful AI product strategy.

This module explores the unique challenges and considerations in productizing AI services, focusing on B2B and B2C models, consistency, and the role of MLOps. Learners will gain insights into building trust, managing feedback loops, and adapting product strategies for AI-driven systems.

This module guides learners through the process of aligning AI solutions with specific verticals, understanding customer behavior, and leveraging peer group insights. It covers strategies for designing AI products tailored to the needs of different industries and emphasizes the importance of KPIs and anomaly detection in real-world applications.

This module explores the distinction between macro and micro AI, focusing on their applications, limitations, and real-world implementations. Learners will gain an understanding of how AI technologies like machine learning, robotics, and expert systems are used in product development. The module also highlights the importance of data, explainability, and safety in AI integration.

This module covers essential concepts for evaluating product success, including value metrics, KPIs, OKRs, and growth strategies. It provides insight into measuring performance from a product and business perspective, and explores tools and techniques for optimizing growth and managing costs effectively.

This module explores how to integrate artificial intelligence into non-ML native products, focusing on strategic planning, communication, and risk management. It equips learners with the skills to navigate the challenges and opportunities of AI adoption in product management. Topics include working with third parties, managing fear and uncertainty, and anticipating potential risks.

This module explores key AI trends across industries, including embedded AI, ethical AI, and autonomous development. Learners will analyze real-world data from Forrester, Gartner, and McKinsey to understand AI adoption, ethical implications, and emerging tools like TuringBots. It provides insights into how AI is reshaping business operations and strategies.

This module explores the process of integrating AI into existing products, focusing on strategic alignment, data readiness, and competitive analysis. Learners will gain insights into prioritizing AI enhancements and identifying key factors that influence successful AI adoption. It provides a structured approach to navigating the challenges and opportunities of AI-driven product development.