CPMAI Mastery System

Why AI Now?

The Seven Patterns of AI

Why AI Projects Fail

Fears and Concerns of Trustworthy AI

The Layers of Trustworthy AI

Iterative and Agile Approaches for AI

Cognitive Project Management in AI

Section 1 Summary

Determine the Problem You Are Solving and If AI Is a Good Fit

Evaluate AI Feasibility

Map Business Problems to AI Patterns

Determine AI Go/No-Go

Determine AI Project ROI and Success Metrics

Scope and Schedule AI Projects

Determine Needs for AI Project Team

Determine Project-Specific AI Risks

Learn How All This Maps to CPMAI Phase I

Section 2 Summary

The Role of Data in AI

Determine Data Quality and Quantity Requirements for AI

Determine Data Sets for AI Projects

Understand Data Privacy, Compliance, and Access Requirements

Coordinate Data Infrastructure and Access Needs

Analytics and Key Data Roles

Learn How All This Maps to CPMAI Phase II

Section 3 Summary

Data Preparation for AI Projects

Data Pipeline in AI Projects

Data Quality Check and Verification

Data Transformation and Synthetic Data

Data Augmentation and Labeling for AI

Data Management for Generative AI Systems

Trustworthy AI in Data Preparation

How It All Maps to CPMAI Phase III

Section 4 Summary

Machine Learning and Models

Model Development

Model Validation

Building Generative AI Systems

Mapping AI Development to CPMAI Phase IV

Section 5 Summary

Model Evaluation

Model Iteration

Model Performance, and Data and Model Drift

Evaluating Models Against Business and Technology KPIs

AI System Monitoring and Management

Explainable and Interpretable AI Systems

Mapping to CPMAI Phase V

Section 6 Summary