Defining Machine Learning (ML) Requirements and Acceptance Criteria Training

This Machine Learning training teaches attendees how to define requirements and acceptance criteria for ML projects. By combining Product Management concepts with ML principles, students learn how to bridge the gap between business goals and technical implementation.

Course Details


2 days


No prior knowledge is presumed.

Target Audience

  • Data Professionals (Data Scientists/Engineers/Analysts, ML Engineers, etc)
  • Managers
  • Project Managers, Product Managers

Skills Gained

  • Identify and define clear business goals and objectives for machine learning projects
  • Create a comprehensive requirements document that aligns business requirements with technical specifications
  • Define measurable acceptance criteria for machine learning projects
  • Communicate the requirements and acceptance criteria to stakeholders in a clear and concise manner
Course Outline
  • Introduction to Machine Learning for Product Managers
    • Understanding the basics of machine learning and its applications.
    • Exploring the role of product management in machine learning projects
    • Thinking about success criteria in the context of experimentation
  • Defining Business Goals and Objectives
    • Identifying the key business objectives and metrics for machine learning projects
    • Translating business goals into measurable outcomes.
    • Anticipating unknown outcomes in machine learning models
  • Gathering Requirements for Machine Learning Projects
    • Techniques for eliciting requirements from stakeholders
    • Capturing and documenting requirements using user stories and use cases
  • Aligning Business Requirements with Technical Specifications
    • Translating business requirements into technical requirements
    • Collaborating with data scientists and engineers to define technical specifications
  • Defining Acceptance Criteria for Machine Learning Projects
    • Understanding the importance of clear acceptance criteria.
    • Defining acceptance tests
    • Identifying relevant metrics
    • Differentiating success criteria for machine learning performance vs. customer value
  • Prioritizing Requirements and Managing Trade-offs
    • Applying prioritization frameworks to rank requirements
    • Managing trade-offs between business goals, technical feasibility, and resource constraints
  • Collaboration and Communication in Machine Learning Projects
    • Effective communication between data professionals and stakeholders
    • Avoiding scope creep
    • Communicating results from experimentation (ie, unknown outcomes before R&D)
    • Presenting requirements and acceptance criteria in a clear and concise manner
  • Applying Best Practices and Case Studies
    • Reviewing best practices for requirements definition and acceptance criteria
    • Analyzing real-world case studies of successful machine learning projects
  • Workshop and Practical Exercises
    • Group Exercise
    • Presentations
    • Peer Review and Feedback
    • Instructor Review and Feedback