ML Projects Training Overview
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 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.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- 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.
Outline
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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
Training Materials
All Machine Learning Project Requirements training students receive comprehensive courseware.
Software Requirements
Detailed setup will be provided upon request.
