AI and Data Product Management Training is a practical, intermediate-level program designed to help professionals plan, build, and manage AI-driven and data-powered products from idea to impact. This course focuses on the unique challenges of managing AI products—where data quality, model behavior, ethics, and continuous improvement are as important as traditional product management practices.

Participants learn how AI and data products differ from traditional software products, how to identify the right AI use cases, and how to align business goals with AI capabilities. The course covers the full AI product lifecycle, including data strategy, model development and deployment awareness, governance, compliance, and responsible AI practices.

Through real-world examples and interactive exercises, learners gain hands-on experience defining AI product requirements, evaluating risks, measuring success with AI-specific metrics, and creating realistic AI product roadmaps. By the end of the course, participants will be equipped to confidently manage AI and data products and collaborate effectively with data scientists, engineers, and business stakeholders.

This course is designed for learners who have a basic understanding of AI or data concepts and want to move into managing AI-driven products. To get the most value from this training, participants should have:

• Familiarity with basic AI or data concepts (no coding required)

• Understanding of business processes or product workflows

• Experience working in roles such as product, business analysis, technology, or operations

• Helpful but not required:

• Exposure to data analytics, machine learning, or AI tools

• Experience collaborating with technical teams (data scientists, engineers)

• Knowledge of product management fundamentals

• This course does not require programming skills and is suitable for professionals who want to lead, manage, or support AI and data product initiatives.

By the end of this course, you will be able to:

• Understand how AI and data products differ from traditional software products

• Identify and evaluate AI use cases aligned with business goals

• Define AI product requirements and success criteria

• Develop and manage data strategies that support AI products

• Understand the AI model lifecycle, including training, deployment, and monitoring

• Collaborate effectively with data scientists, engineers, and business stakeholders

• Apply principles of responsible AI, ethics, fairness, and bias mitigation

• Navigate regulatory and compliance requirements such as GDPR and CCPA

• Measure AI product success using AI-specific metrics (precision, recall, F1 score, ROI)

• Create and maintain AI product roadmaps and manage iterative improvements

• Assess risks related to model drift, data quality, and governance

• Communicate AI product value and impact to technical and non-technical stakeholders

This course prepares learners for roles that bridge business strategy, data, and AI execution. After completing the training, learners will be better prepared for positions such as:

• AI Product Manager

• Data Product Manager

• Product Manager (AI / Data Products)

• Technical Product Manager (AI-focused)

• Business Analyst – AI & Analytics

• Product Owner (AI & Data Platforms)

• AI Program or Project Manager

• Digital Transformation Manager

• Product Strategy Consultant (AI & Data)

• Analytics or Data Strategy Manager

Module 1: Introduction to AI & Data Product Management

• What is AI and Data Product Management

• Differences between traditional products and AI-driven products

• Types of AI-powered products (predictive analytics, NLP, computer vision, recommendation systems)

• Role and responsibilities of an AI & Data Product Manager

• Key skills and challenges in managing AI-powered products

Module 2: Data Strategy & AI Development Lifecycle

• Data as a product: ownership, quality, and governance

• Data collection, processing, and infrastructure considerations

• Bias, ethics, and responsible data practices

• AI model lifecycle: training, testing, and validation

• Model deployment, monitoring, and continuous improvement (MLOps overview)

• Case study discussion: real-world AI product implementations

Module 3: Building & Managing AI-Driven Products

• Identifying and prioritizing AI use cases

• Mapping business problems to AI capabilities

• Feasibility assessment and risk analysis

• Defining AI product requirements

• AI development lifecycle from concept to production

• Exercise: AI product ideation and feature definition workshop

Module 4: AI Governance, Compliance & Ethics

• Responsible AI principles and frameworks

• Managing AI bias and fairness

• Privacy and security considerations

• Regulatory compliance: GDPR, CCPA, and AI regulations

• Model interpretability, transparency, and risk mitigation

• Exercise: Ethical dilemmas in AI – real-world scenarios

Module 5: AI Product Metrics & Roadmap Planning

• Measuring AI product success and performance

• Key AI metrics: precision, recall, F1 score, ROI, confusion matrix

• User adoption and business impact measurement

• Creating and maintaining an AI product roadmap

• Managing experiments, iterations, and continuous improvement

• Scaling AI solutions across the organization