CPMAI Mastery System

CPMAI Methodology Phase 1: Business Understanding

CPMAI Methodology Phase 2: Data Understanding

CPMAI Methodology Phase 3: Data Preparation

CPMAI Methodology Phase 4: Model Development

CPMAI Methodology Phase 5: Model Evaluation

CPMAI Methodology Phase 6: Model Operationalization

CPMAI Methodology Phase Goals

CPMAI Methodology Go/No Go Decisions

Understanding of AI Fundamentals and Evolution

Evaluating AI Applications and Patterns

Applying Machine Learning Fundamentals

Differentiating AI Project Management Approaches

Executing the Business Understanding Phase

Managing the Data Understanding Phase

Applying Classification and Clustering Algorithms

Implementing Neural Networks and Deep Learning

Leveraging Generative AI and Large Language Models

Selecting Machine Learning Tools and Platforms

Managing Data Fundamentals and Big Data Concepts

Implementing Data Governance and Management

Executing Data Preparation and Transformation

Evaluating Model Performance and Accuracy

Deploying Models for Production Environments

Establishing Ethical, Responsible, and Trustworthy AI Foundations

Implementing AI Privacy and Security

Ensuring AI Transparency and Explainability

Navigating AI Regulations and Frameworks

Conducting Model Evaluation and Maintenance

Determining the Approaches for Model Development

Engineering Data Pipelines

Coordinating the Data Preparation