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CPMAI v7 does not require any prior work experience, technical knowledge or AI experience, certifications, or
prerequisites to enroll in the course and take the exam.
Audience
- Project, program, and product managers
- Data professionals
- Technologists, Consultants, and IT professionals
- Directors and Senior Managers leading AI initiatives
The CPMAI is a vendor-agnostic, data-centric, AI-specific, iterative methodology for running and
managing AI, Machine Learning (ML), and cognitive technology projects.
The CPMAI v7 examination is a vital part of the activities leading to earning a professional
certification; thus, it is imperative that the CPMAI v7 examination reflects a p r o v e n vendor-agnostic
best practice methodology for artificial intelligence (AI), Machine Learning (ML), advanced data analytics,
intelligent automation and projects of any size. All the questions on the examination have been written
and reviewed by AI subject matter experts. These questions are mapped against the CPMAI v7
Examination Content Outline to ensure that an appropriate number of questions are in place for a valid
examination.
By the end of this training, participants will be able to:
- CPMAI course ensures you have access to the most current practices and strategies for managing AI projects by covering the following topics. PMI’s comprehensive self-paced format includes directed multimedia content, a guided review of Exam Content Outline (ECO) references, and structured independent study activities, allowing learners to progress at their own pace while building a deep understanding of the material.
Course Investment
IDR65.229.738 for 1 participant (one batch) for inhouse private training
Additional IDR11,819,575 for certification per participant
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Domain I AI Fundamentals
- Understanding of AI Fundamentals and Evolution
- Evaluating AI Applications and Patterns
- Applying Machine Learning Fundamentals
Domain II CPMAI Methodology
- Differentiating AI Project Management Approaches
- Executing the Business Understanding Phase
- Managing the Data Understanding Phase
- Coordinating the Data Preparation Activities
- Determining the Approaches for Model Development
- Conducting Model Evaluation and Maintenance
Domain III Machine Learning
- Applying Classification and Clustering Algorithms
- Implementing Neural Networks and Deep Learning
- Leveraging Generative AI and Large Language Models (LLMs)
- Selecting Machine Learning Tools and Platforms
Domain IV Data for AI
- Managing Data Fundamentals and Big Data Concepts
- Implementing Data Governance and Management
- Engineering Data Pipelines for AI
- Executing Data Preparation and Transformation
Domain V Managing AI
- Evaluating Model Performance and Accuracy
- Deploying Models for Production Environments
Domain VI Trustworthy AI
- Establishing Ethical, Responsible, and Trustworthy AI Foundations
- Implementing AI Privacy and Security
- Ensuring AI Transparency and Explainability
- Navigating AI Regulations and Frameworks
Summary and Next Steps