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
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- Day 1: AI Projects Overview0 Items• What makes AI projects unique
• Common challenges
• Project phases
• Key team roles
• Stakeholder alignment - Day 2: Requirements & Data Planning0 Items• Understanding problem statements
• Data sourcing
• Data quality assessment
• Labeling strategies
• Documentation best practices - Day 3: Model Development Workflow0 Items• Experimentation cycles
• Tracking model performance
• Tooling overview
• Integration with engineering teams
• Case study - Day 4: Deployment & Monitoring0 Items• Model deployment considerations
• APIs and production systems
• Monitoring models in the wild
• Retraining strategies
• Change management - Day 5: Evaluation & Roadmapping0 Items• Success metrics
• Post-project analysis
• Scaling AI capabilities
• Budgeting and risk mitigation
• Capstone workshop
Instructor
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