Introduction
Managing AI projects requires a unique blend of technical understanding and strategic planning. This course equips project managers with the knowledge needed to oversee AI initiatives from concept to deployment. Participants will learn about data requirements, model development workflows, and cross-functional collaboration. Emphasis is placed on risk management, timelines, and evaluation metrics. By the end, attendees will confidently guide AI projects toward successful outcomes.
Course Objectives
- Understand the AI project lifecycle
- Learn how to gather requirements for AI systems
- Coordinate teams of data scientists and engineers
- Manage risks and monitor progress
- Evaluate AI project success
Target Audience
- Project managers
- Product managers
- Team leads
- Technical program managers
- Professionals involved in AI delivery
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: AI Projects Overview• What makes AI projects unique
• Common challenges
• Project phases
• Key team roles
• Stakeholder alignment0 - Day 2: Requirements & Data Planning• Understanding problem statements
• Data sourcing
• Data quality assessment
• Labeling strategies
• Documentation best practices0 - Day 3: Model Development Workflow• Experimentation cycles
• Tracking model performance
• Tooling overview
• Integration with engineering teams
• Case study0 - Day 4: Deployment & Monitoring• Model deployment considerations
• APIs and production systems
• Monitoring models in the wild
• Retraining strategies
• Change management0 - Day 5: Evaluation & Roadmapping• Success metrics
• Post-project analysis
• Scaling AI capabilities
• Budgeting and risk mitigation
• Capstone workshop0
Instructor
Lessons:
