Exclusive 2-Day Course to Effectively Lead AI & ML Initiatives with Confidence

Virtual Classroom Live

Leading AI and ML Projects

This intensive two-day course is designed for project managers seeking to lead artificial intelligence (AI) and machine learning (ML) initiatives with success. Participants will explore the unique challenges of AI/ML projects, master AI-specific terminology, assess risks, and lead cross-functional teams through complex project lifecycles. Leveraging MLOps principles, participants will gain the skills to scope, plan, monitor, and execute AI/ML projects while ensuring responsible governance and business alignment. 

Foundational understanding of project management concepts required

We’ve teamed up with Global Knowledge (GK)—a worldwide leader in professional IT training and certifications and part of the Skillsoft family to bring you targeted, high-impact, industry-relevant courses to help you advance your skills and achieve your career goals.

While you'll enroll and check out through Codecademy, your course experience—including instruction, materials, and support—will be delivered by Global Knowledge. Global Knowledge offers courses designed to help you sharpen your skills, boost your credentials, and move your career forward.

Do you lead AI/ML initiatives?

Perfect for project managers ready to take charge of AI/ML efforts and guide teams through the unique challenges of data-driven development.

Are you navigating complex AI projects?

Designed for TPMs who need to align cross-functional teams, ensure technical feasibility, and maintain momentum across the AI/ML lifecycle.

Are you working on AI-powered solutions?

Ideal for product managers aiming to define realistic roadmaps, balance stakeholder expectations, and deliver AI-driven features with confidence.

Do you manage data-centric initiatives?

Tailored for business leaders responsible for overseeing AI/ML investments, governance, and long-term value creation from machine learning strategies.

Use AI-specific methodologies to plan and manage unique workflows, data needs, and outcomes.

Define project goals, success metrics, deliverables, and timelines tailored to AI/ML initiatives.

Explore how AI/ML projects differ from traditional software and grasp key concepts and terminology.

Effectively manage collaboration between data scientists, engineers, and business stakeholders.

Align AI/ML project outcomes with executive priorities and communicate project scope clearly.

Identify potential challenges like bias, data quality issues, and model drift, and plan mitigation strategies.

Confidently discuss terms like supervised learning, neural networks, inference, and model training.

Understand the iterative, data-dependent nature of AI/ML lifecycles compared to traditional software.

Apply MLOps principles to streamline development, testing, deployment, and monitoring of models.

Determine whether an AI/ML project is viable based on available data, tools, talent, and time.

Divide large AI/ML initiatives into manageable phases with clear milestones and accountability.

Use both technical and business KPIs to monitor progress, success, and areas needing attention.

Develop strategies to adapt to evolving project conditions, data issues, or shifting business goals.

Translate AI/ML results and risks into business terms for both technical and non-technical audiences.

Implement policies and frameworks for ethical, transparent, and compliant AI project delivery.

Key concepts

1

AI/ML-Specific Project Management

2

Cross-Functional Leadership and Team Collaboration

3

Risk Management in AI/ML

4

MLOps and Sustainable AI Deliverya

5

Stakeholder Communication and AI Governance

Leading AI and ML Projects

1

Key AI/ML Terminology 

2

AI/ML vs. Traditional Software Projects 

3

Project Lifecycle Overview 

1

Feasibility Assessment 

2

Defining Deliverables and Success Metrics 

3

Resource and Data Requirements 

1

Identifying AI-Specific Risks (Model Drift, Bias, Data Quality) 

2

Mitigation Frameworks and Regulatory Considerations 

1

Managing Executive Expectations 

2

Translating Technical Results for Business Stakeholders 

3

Communicating AI Limitations and Uncertainties 

1

Technical and Business Metrics for Progress Tracking 

2

AI Governance and Responsible AI Policies 

3

MLOps as a Framework for Sustainable AI Delivery 

1

AI/ML Deployment Models 

2

Introduction to Prompt Engineering for Project Managers 

3

Managing Production AI Systems