CPMAI Mastery System

Cognitive Project Management In AI

CPMAI Iterative, Six-Phase Approach

Knowledge Check

Reminder: The PMI CPMAI Workbook

Summary

Intelligent Machines

What Are AI and Cognitive Technologies Best Suited For?

Well-Suited Applications of Generative AI

Augmented Intelligence

What Are AI and Cognitive Technologies Not Suited For?

PMI CPMAI™ Workbook: Use Case

PMI CPMAI™ Workbook Checkpoint: AI Versus Non-AI Approaches

Knowledge Check

Understanding AI's Role Through the DIKUW Pyramid

Strategic Questions for CPMAI Phase I Business Understanding

PMI CPMAI™ Workbook Checkpoint: Solving a Business Problem

A Guide to Project Fit Using the Seven Patterns

A Business-Centric Guide to the Seven Patterns of AI

PMI CPMAI™ Workbook Checkpoint: AI Pattern

Key Questions to Address at the Start—Not the Finish

The AI Project Go/No-Go Decision

Knowledge Check

Planning for AI Project Success

PMI CPMAI™ Workbook Checkpoint: Cost and Benefit Analysis

Avoiding Failures: Mismatch and Vendor Hype

Determining Acceptable Levels of Performance

PMI CPMAI™ Workbook Checkpoint: AI System Performance

What Are the Different Times to ROI for Different Types of AI Projects?

Case Where ROI Was Not Established Up Front

Potential Pitfall: Using Models or Data That Do Not Reflect Reality

Example of Model Misalignment With Real-World Conditions (Andrew Ng Radiology Case Study)

Knowledge Check

Prioritizing and Scoping AI Projects: Initial Problem Identification and Appropriate Fit/Scope

The Proof-of-Concept Trap

Why Even Build a Model?

Building on the Work of Others

Pretrained Models

Using Foundation Models: Large, General-Use Models

Accelerating AI Projects With GenAI and Foundation Models

AI Project Team: What to Look for With AI Teams

PMI CPMAI™ Workbook Checkpoint: Schedule and Resource Requirements

Trustworthy AI Requirements

Requirements for AI System Transparency

Visibility Into System Design and Methods

XAI Compliance

What Are the Challenges and Drawbacks of Generative AI?

PMI CPMAI™ Workbook Checkpoint: Trustworthy AI Requirements

Knowledge Check

CPMAI Phase I Applied: IT Help Desk AI Assistant

CPMAI Phase I Applied: Internal Policy GenAI Solution

CPMAI Phase I Applied: Insurance Claim Fraud Detection

CPMAI Phase I Applied: AI for Employee Promotions

CPMAI Phase I Applied: Automated Email Forwarding System

PMI CPMAI™ Workbook Checkpoint: AI Go/No-Go

CPMAI Phase I Go/No-Go

Summary

Data Fuels Intelligence

Data-First Approach

Big Data

Vs of Big Data: Volume

Vs of Big Data: Velocity

Vs of Big Data: Variety

Vs of Big Data: Veracity

Big Data: Lessons Learned

Applying Big Data Approaches to AI

Knowledge Check

Failure Reason: Data Quantity and Quality Issues

Data Quantity Issues

Data Quality Issues

AI-Specific Aspects of Data Understanding

Knowledge Check

Identifying Data Sets for ML Data Collection

What Is Training Data?

What Are Structured, Unstructured, and Semistructured Data?

The Untapped Value of Unstructured Data

Does AI Need a Lot of Data?

Running AI Projects With Small Amounts of Data

What Is Ground Truth Data?

What Is Data Management?

What Is a Data Management Plan?

Knowledge Check

PMI CPMAI Workbook Checkpoint: Data Determination

PMI CPMAI Workbook Checkpoint: Use of Pretrained Model

What Is Data Governance?

What Is Data Stewardship?

Data Stewards and Data Custodian

What Is Informational Bias?

Knowledge Check

The Data Life Cycle

What Is Data Quality Management?

Knowledge Check

What Is Analytics?

Data Science Versus Data Analytics

Knowledge Check

Moving Beyond CPMAI Phase II

CPMAI Phase II Go/No-Go

When to Iterate Back to Previous CPMAI Phases

Knowledge Check

CPMAI Phase II Applied: IT Help Desk AI Assistant

CPMAI Phase II Applied: Internal Policy Generative AI Solution

CPMAI Phase II Applied: Insurance Claim Fraud Detection

CPMAI Phase II Applied: Real-Time Energy Consumption Predictio

Summary

Data Preparation Concepts

Knowledge Check

Data Engineering and Data Pipelines

Data Engineering Concepts

Knowledge Check

Moving Data Around

Data-Handling Approaches

Knowledge Check

Data Collection and Ingestion

Data Sources for Ingestion

Data Ingestion Approaches

Knowledge Check

Data Preparation Pipelines

Pipeline Complexity

Data Preparation Pipelines for Training and Inference

Developing Inference Pipelines

Knowledge Check

Data Cleansing and Enhancement

Ways to Improve Data Quality and Accuracy

Cleaning and Enhancing Data for AI

PMI CPMAI™ Workbook Checkpoint: Data Cleansing and Enhancement

Data Sampling: Introduction

Splitting Data Sets

PMI CPMAI™ Workbook Checkpoint: Data Selection

Data Transformation and Augmentation for AI

Techniques to Increase Image Data Quantity

Synthetic Data: An Approach to Enhancing Data

Data Augmentation Terms

Knowledge Check

Purpose and Examples of Data Labeling

Data Labeling: Approaches

Knowledge Check

Can Generative AI Help With Data Prep?

Using GenAI for Data Augmentation

PMI CPMAI™ Workbook Checkpoint: Data Augmentation and Enhancement

Implementing Trustworthy AI in Data Preparation

Knowledge Check

Data Governance: Foundational Framework

Data Governance: Roles and Responsibilities

Data Governance: Compliance and Security

Data Governance: Data Life Cycle Management

Knowledge Check

CPMAI Phase III Ensuring Readiness

CPMAI Phase III Applied: IT Help Desk AI Assistant

CPMAI Phase III Applied: Internal Policy GenAI Solution

CPMAI Phase III Applied: Insurance Claim Fraud Detection

CPMAI Phase III Applied: Audio Sentiment Analysis for Call Centers

Data Engineering Questions to Answer in This Phase

CPMAI Phase III Go or No-Go

When to Iterate Back to Previous CPMAI Phases

Section 4 Summary

Introduction to Machine Learning

Machine Learning Algorithm and Machine Learning Model

Machine Learning Algorithm Basics

Knowledge Check

Machine Learning Algorithm: Other Models

What Is AI Model Development?

Automated Machine Learning

Knowledge Check

PMI CPMAI™ Workbook Checkpoint: Model Method

Transfer Learning and Third-Party Models

PMI CPMAI™ Workbook Checkpoint: Pretrained Models, Foundation Models, and GenAI

Introduction to Model Validation

Understanding Learning: Generalizing to New Data

Knowledge Check

Building GenAI Systems

Enhancing LLMs With Retrieval-Augmented Generation

Fine-Tune LLMs

PMI CPMAI™ Workbook Checkpoint: Build and Adjust Model

Knowledge Check

CPMAI Phase IV Applied: IT Help Desk AI Assistant

CPMAI Phase IV Applied: Internal Policy GenAI Solution

CPMAI Phase IV Applied: Insurance Claim Fraud Detection

CPMAI Phase IV Applied: AI for Automated Loan Approval

CPMAI Phase IV Go/No-Go

Section 5 Summary

Framing the Purpose

Why Model Evaluation Matters

When Model Evaluation Falls Short

How to Evaluate a Model Effectively

PMI CPMAI™ Workbook Checkpoint: Model Evaluation

Why Iteration Is Important

Core Concepts and Foundations

Timing the Retraining and Executing the Process

Knowledge Check

PMI CPMAI™ Workbook Checkpoint: Model Iteration

Understanding Data Drift

Understanding Model Drift

Responding, Measuring and Ongoing Oversight

KPIs Overview

Evaluating Against Business and Technical Goals

Knowledge Check

Ensuring Efficient Use of Resources

Understanding AI Audit Trails

Ensuring Auditability and Traceability

Integrating With DevSecOps

Knowledge Check

AI Transparency

Limits of Explainability

Knowledge Check

Bridging the Gap With Interpretable AI

Interpretable AI From Theory to Impact

Phase V Preparing for Deployment

Phase V Planning for Improvement

Phase V Iterating and Revisiting Earlier Phases

Knowledge Check

CPMAI Phase V Applied: IT Help Desk AI Assistant

CPMAI Phase V Applied: Internal Policy GenAI Solution

CPMAI Phase V Applied: Insurance Claim Fraud Detection

CPMAI Phase V Applied: Telecom Customer Churn Prediction

Section 6 Summary

The “Inference” Phase of an AI Project

What Is Operationalization?

Model Operationalization and Model Life Cycle Questions to Answer

Knowledge Check

The Four Different AI Technology Environments

Model Development and Training Environment

Big Data/Data Engineering Environment

Model Scaffolding Environment

Model Operationalization Environment

Knowledge Check

Interacting With AI Models: Batch Prediction

Interacting With AI Models: Microservices

Interacting With AI Models: Real-Time Prediction

Interacting With AI Models: Stream Learning

Cold Path Versus Hot Path Analytics

Where to Operationalize Models: On Premises

Where to Operationalize Models: Edge Device

Where to Operationalize Models: Cloud ML

Knowledge Check

Implementing GenAI in Production

Self-Hosted Versus API-Hosted GenAI Models

The Risks of GenAI in Production

Knowledge Check

PMI CPMAI™ Workbook Checkpoint: Model Operationalization

Failure Reason: AI Project Life Cycles Are Continuous

AI Life Cycle Challenges: Real-Life Example

Model Life Cycle Management

Managing the Data Life Cycle

What Is Development and Operations?

What Is Machine Learning Operations?

Knowledge Check

What Is Model Governance?

Model Deployment With Governance Framework

What Is Model Monitoring?

Knowledge Check

Trustworthy AI Considerations

AI System Safety and Reliability

Malicious AI

Securing Machine Learning Models

PMI CPMAI™ Workbook Checkpoint: Model Monitoring and Management

Regulating Concerns of AI

Resolving Issues of Ethical and Trustworthy AI

Trustworthy AI Framework

Knowledge Check

The Limits of AI Technology

CPMAI Phase VI Go/No-Go

PMI CPMAI™ Workbook Checkpoint: The Next Iteration

CPMAI Phase VI Applied: IT Help Desk AI Assistant

CPMAI Phase VI Applied: Internal Policy Generation

CPMAI Phase VI Applied: Insurance Claim Fraud Detection

CPMAI Phase VI Applied: GenAI Content Creator

Section 7 Summary