When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Defining Machine Learning Requirements and Acceptance Criteria Training
This Defining Machine Learning (ML) Requirements and Acceptance Criteria training course teaches attendees how to translate business requirements into technical specifications that ensure ML models...
Read MoreThis Defining Machine Learning (ML) Requirements and Acceptance Criteria training course teaches attendees how to translate business requirements into technical specifications that ensure ML models are aligned with the needs of the business. Participants learn how to manage the entire ML project lifecycle (from defining the business goals to delivering the final product) and communicate effectively with technical and non-technical stakeholders.
Skills Gained
- Identify and define clear business goals and objectives for Machine Learning projects
- Elicit requirements from stakeholders
- Translate business requirements into technical requirements
- Define acceptance criteria
- Prioritize requirements and manage trade-offs
- Collaborate and communicate effectively with stakeholders
- Apply best ML project management practices
Prerequisites
No prior experience is presumed.
Course Details
Training Materials
All Machine Learning Project Requirements training students receive comprehensive courseware.
Software Requirements
Detailed setup will be provided upon request.
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
- 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
- Conclusion