Course Catalog  > Data Science, AI, and ML  > AI and Generative AI  > AI for Managers
WA3571

AI Project Management Training

This AI Project Management course teaches professionals how to navigate the unique challenges and opportunities of AI projects. Participants gain a deep understanding of the AI project lifecycle and learn to define clear roles and responsibilities, effectively mitigate AI-specific risks, engage stakeholders throughout the project, and implement strategies for continuous improvement.

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By the end of this course, attendees are prepared to successfully lead AI projects.

Course Details

Duration

2 days

Prerequisites

  • Experience with technology project management and delivery
  • Experience in project management or leadership roles
  • Familiarity with AI and machine learning pipelines is recommended but not required

Skills Gained

  • Master the AI project lifecycle, from conception to deployment, enabling successful project planning, execution, and oversight
  • Develop robust risk management strategies and stakeholder engagement plans to address challenges and ensure project success
  • Optimize resource allocation, manage technical and human resources effectively, and foster seamless collaboration across project roles
  • Implement effective change management practices, including communication strategies, to overcome resistance and cultivate a culture of continuous learning
Course Outline
  • The AI Project Lifecycle
    • Understand AI project phases
    • Identify AI business opportunities
    • Ideation: Identifying business problems
    • Data Collection & Preparation: Data requirements and quality
    • Model Selection & Training: Choosing and training AI models
    • Evaluation & Refinement: Model performance and bias detection
    • Deployment & Maintenance: Integrating AI models into production
  • Project Scoping and Management
    • Develop comprehensive AI project plans
    • Align AI projects with business objectives
    • Defining Project Goals: Setting SMART goals
    • Identifying Resources: Estimating technical and human resources
    • Developing a Project Plan: Timelines and management
    • Project Management Tools & Techniques: Agile, Waterfall methodologies
  • AI Project Roles and Responsibilities
    • Define roles within an AI project team
    • Understand the responsibilities of each role
    • Executive Sponsor: Project champion and leader role
    • Project Manager: Team leadership and resource management
    • Data Scientist: Data preparation and model development
    • AI Engineer: Model deployment and scalability
    • Domain Expert: Business context and solution evaluation
  • Risk Management in AI Projects
    • Identify potential risks in AI projects
    • Develop risk mitigation strategies
    • Risk Identification: Common AI project risks
    • Mitigation Strategies: Techniques to address risks
    • Contingency Planning: Preparing for unforeseen issues
  • Stakeholder Engagement and Change Management
    • Engage and manage project stakeholders
    • Develop effective communication strategies
    • Identifying Stakeholders: Mapping stakeholders and their interests
    • Communication Strategy: Plans for stakeholder engagement
    • Managing Expectations: Techniques for effective communication
    • Change Management: Addressing resistance and fostering learning
  • AI Solution Delivery and Continuous Improvement
    • Deploy AI models effectively
    • Establish robust monitoring and governance frameworks
    • Model Deployment Strategies: On-premise, cloud, hybrid
    • Monitoring and Logging: Tracking performance and security
    • Governance and Explainability: Responsible AI practices
    • Continuous Improvement: Feedback loops for refinement