Product Management with Artificial Intelligence

Learn via : Virtual Classroom / Online
Duration : 2 Days
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Description

    “Product Management with Artificial Intelligence” training program is a comprehensive corporate workshop designed to equip participants with essential AI concepts, product management strategies, and hands-on skills to effectively manage AI-driven projects. Offered as one- or two-day sessions, the program covers AI fundamentals, opportunity validation, lifecycle management, and product strategy.
    For the two-day sessions, the learning experience is enriched with interactive activities such as concept mapping, team simulations, and real-world AI case studies. Participants collaborate in design-thinking workshops to develop AI solutions aligned with business objectives. Each session concludes with practical assignments to reinforce key takeaways and support skill development.

    The Product Management with Artificial Intelligence training not only provides participants with a solid grasp of technical details but also teaches them how to leverage AI in strategic decision-making processes.

    Audience

    • Business Analysts, Systems Analysts, Project Managers, Team Leaders/Managers, and Enterprise Architects
    • Individuals holding the Product Owner (PO) role in Scrum teams
    • Those looking to improve their knowledge and skills in Business Analysis and Project Scope Management, and enhance business performance
    • Mid to Senior-level IT Managers managing Business Analysis teams or processes
    • UX Specialists
    • Software Experts
    • Test Engineers and Quality Assurance Experts

Outline

Module 1: AI Fundamentals and Product Management

  • Machine Learning, Deep Learning, and Natural Language Processing (NLP): How these AI approaches are utilized in product development.
  • Types of AI: The impact of various algorithm types, such as supervised, unsupervised, and reinforcement learning, on identifying product opportunities.
  • The Technical Role of an AI Product Manager: Managing model selection, dataset requirements, and model evaluation metrics.

Module 2: AI Product Development and Lifecycle

  • Validating AI Opportunities: Techniques for problem definition and hypothesis testing to assess AI solution fit.
  • AI Product Development Steps: Managing processes such as data collection, data cleaning, model training, and model deployment.
  • MLOps: Strategies for model maintenance and updates, as well as continuous integration and delivery.

Module 3: Strategy and Success Measurement

  • Success Metrics: Defining OKRs (Objectives and Key Results), KPIs (Key Performance Indicators), and North Star metrics for AI products.
  • AI Model Performance Testing: Interpreting metrics such as precision, recall, and F1 score, and optimizing model accuracy.
  • Model Bias and Fairness: Control processes to ensure AI models are ethical and equitable.

Prerequisites

Participants are expected to be familiar with basic concepts such as product development, product strategy, and the product lifecycle.