Leading AI and ML Projects [GK821855]

OVERVIEW

This 2-day course is designed for project managers who want to effectively lead artificial intelligence and machine learning initiatives.

Learners will identify the unique characteristics and challenges of AI/ML projects, understand common AI/ML terminology, evaluate and mitigate AI-specific risks, and lead and communicate effectively with cross-functional teams as well as key business stakeholders. Using MLOps principles to guide project planning and execution, at the end of this course you will be able to design comprehensive project plans that address the unique challenges of AI/ML development, assess the feasibility and resource requirements of proposed AI/ML initiatives, break down complex AI/ML projects into manageable phases and deliverables, and critique project progress using appropriate technical and business metrics.

OBJECTIVES

• Identify the unique characteristics and challenges of AI/ML projects
• Apply appropriate methodologies for AI/ML project management
• Effectively scope and plan AI/ML initiatives
• Manage stakeholder expectations around AI/ML outcomes
• Lead cross-functional teams of data scientists, engineers, and domain experts
• Evaluate and mitigate AI-specific risks
• Monitor and measure AI/ML project success
• Describe the key roles and responsibilities within AI/ML project teams
• Explain the differences between traditional software and AI/ML project lifecycles
• Interpret common AI/ML terminology including neural networks, supervised/unsupervised learning, model training, and inference
• Implement appropriate project management methodologies for different types of AI/ML initiatives
• Demonstrate effective communication strategies with technical and non-technical stakeholders
• Use MLOps principles to guide project planning and execution
• Break down complex AI/ML projects into manageable phases and deliverables
• Differentiate between various types of AI/ML project risks and their potential impacts
• Examine data requirements and quality criteria for ML model development
• Assess the feasibility and resource requirements of proposed AI/ML initiatives
• Critique project progress using appropriate technical and business metrics
• Judge the effectiveness of risk mitigation strategies in AI/ML contexts
• Design comprehensive project plans that address the unique challenges of AI/ML development
• Develop stakeholder management strategies that account for AI/ML uncertainties
• Formulate data-driven decision-making frameworks for project governance

AUDIENCE

This 2-day course is designed for project managers who want to effectively lead artificial intelligence and machine learning initiatives. The course assumes foundational project management knowledge and focuses on the unique aspects of AI/ML project leadership.

CONTENT

• Essential AI/ML Terminology and Concepts
• Understanding AI/ML Project Fundamentals
• AI/ML Project Lifecycle and Methodologies
• Scoping and Planning AI/ML Projects
• Building and Managing AI/ML Teams
• Risk Management in AI/ML Projects
• Stakeholder Management and Communication
• Monitoring and Measuring Success
• Prompt Engineering for Project Managers
• Deployment and Production Considerations