Program Structure

Week 1: Introduction to AI Project Management

• Overview of AI project management principles and best practices.
• Understanding the AI project lifecycle: planning, development, deployment, and maintenance.
• Exploring the roles of AI project managers and teams.
• Hands-on exercise: Mapping out the phases of an AI project lifecycle.

Week 2: Project Planning and Scope Management

• How to define project scope, objectives, and deliverables.
• Understanding AI project requirements: setting clear goals, KPIs, and success metrics.
• Creating project timelines, budgets, and resource allocation plans.
• Hands-on exercise: Develop a project plan for a hypothetical AI project.

Week 3: Team Collaboration and Agile Methodology

• Building and managing cross-functional teams for AI projects.
• Applying Agile and Scrum methodologies to AI project management.
• Handling stakeholder communication and feedback loops in an AI project.
• Hands-on exercise: Create an Agile framework for an AI project with team roles and timelines.

Week 4: Data Management and Model Development

• Managing data collection, preparation, and quality assurance for AI projects.
• Understanding model development workflows: training, testing, and validation.
• Handling version control, model monitoring, and performance tuning.
• Hands-on exercise: Create a data pipeline and test model development in a simulated environment.

Week 5: AI Project Deployment and Scaling

• Strategies for deploying AI models into production environments.
• Integration with existing systems and continuous model monitoring.
• Scaling AI solutions across organizations and managing AI infrastructure.
• Hands-on exercise: Simulate deployment and scaling of a machine learning model.

Week 6: Risk Management and Ethical Considerations

• Identifying, assessing, and mitigating risks in AI projects.
• Understanding the ethical implications of AI: bias, fairness, and accountability.
• Managing data privacy, security, and regulatory compliance in AI projects.
• Hands-on exercise: Develop a risk management plan for an AI project, addressing ethical concerns.

Week 7: Communication and Stakeholder Management

• Techniques for effective communication with stakeholders in AI projects.
• Managing stakeholder expectations, reporting progress, and addressing issues.
• Creating project documentation, reports, and presentations for AI projects.
• Hands-on exercise: Prepare a project progress report and presentation for a mock AI project.

Week 8: Final Project: AI Project Simulation

• Work on a comprehensive final project that simulates the entire lifecycle of an AI project.
• Apply the learned concepts in project planning, team management, model development, deployment, and risk management.
• Present the final project, including key findings, challenges, and lessons learned.