PMI Certified Professional In Managing AI Course in Port Elizabeth

PMI Certified Professional in Managing AI Course Outline

Domain I: Support Responsible and Trustworthy AI Efforts

Module 1: Oversee Privacy and Security Plan

• Establish Data Governance Protocols for Personally Identifiable Information (PII)
• Implement Encryption and Access Controls for AI Training Data
• Conduct Privacy Impact Assessments for AI Model Deployment
• Ensure Compliance with GDPR, CCPA, and Other Data Protection Regulations
• Design Secure Data Handling Procedures Throughout the AI Lifecycle

Module 2: Manage AI/ML Transparency (eg, Data Selection, Algorithm Selection)

• Document Model Selection Criteria and Decision Rationale
• Create Transparent Reporting on Data Sources and Preprocessing Steps
• Establish Explainability Requirements for Stakeholder Communication
• Maintain Audit Trails for Algorithmic Decision Making Processes
• Implement Model Interpretability Tools and Techniques

Module 3: Conduct Bias Checks (eg, Model, Data, Algorithm)

• Analyse Training Data for Demographic and Representation Imbalances
• Perform Fairness Testing Across Different Population Groups
• Implement Bias Detection Metrics and Monitoring Systems
• Review Model Outputs for Discriminatory Patterns
• Apply Bias Mitigation Techniques During Model Development

Module 4: Monitor Regulatory and Policy Compliance

• Track Evolving AI Regulations and Industry Standards
• Ensure Adherence to Sector Specific Compliance Requirements
• Coordinate with Legal and Compliance Teams on AI Governance
• Implement Compliance Monitoring and Reporting Mechanisms
• Maintain Documentation for Regulatory Audits and Reviews

Module 5: Manage Accountability Documentation and Audit Trail

• Create Comprehensive Records of AI Model Development Decisions
• Establish Version Control for Models, Data, and Training Processes
• Document Stakeholder Approvals and Go/No Go Decision Points
• Maintain Chain of Custody Records for Training and Test Data
• Prepare Accountability Reports for Executive and Regulatory Review

Domain II: Identify Business Needs and Solutions

Module 6: Identify Problem to be Solved (eg, Needs, Persona)

• Conduct Stakeholder Interviews to Understand Business Pain Points
• Analyse Existing Processes to Identify Automation Opportunities
• Define Target User Personas and Use Cases for AI Solutions
• Map Business Problems to Appropriate AI Patterns and Approaches
• Validate Problem Statements with Subject Matter Experts

Module 7: Evaluate Initial AI Feasibility

• Assess Technical Viability of Proposed AI Solutions
• Analyse Data Availability and Quality for Model Training
• Evaluate Computational Resource Requirements and Constraints
• Review Organisational Readiness for AI Implementation
• Compare AI Approaches Against Traditional Solution Alternatives

Module 8: Conduct Risk Assessment (eg, Security, Safety, Ethics)

• Identify Potential Failure Modes and Safety Implications
• Assess Cybersecurity Vulnerabilities in AI Systems
• Evaluate Ethical Implications of AI Decision Making
• Analyse Reputational and Business Continuity Risks
• Develop Risk Mitigation Strategies and Contingency Plans

Module 9: Develop AI Project Scope Statement

• Define Project Boundaries and Deliverables for AI Initiatives
• Establish Success Criteria and Performance Metrics
• Identify In-Scope and Out-of-Scope Functionality
• Document Assumptions and Constraints for AI Implementation
• Align Scope with Business Objectives and Resource Availability

Module 10: Determine ROI

• Calculate Expected Benefits from AI Solution Implementation
• Estimate Total Cost of Ownership Including Infrastructure and Maintenance
• Develop Business Case with Financial Justification
• Establish Metrics for Measuring Return on Investment
• Create Cost Benefit Analysis for Stakeholder Decision Making

Module 11: Manage Adoption/Integration Risks

• Assess Organisational Change Management Requirements
• Identify Potential User Resistance and Adoption Barriers
• Plan Integration with Existing Systems and Workflows
• Develop Training and Communication Strategies for End Users
• Monitor Adoption Metrics and Address Implementation Challenges

Module 12: Draft AI Solution

• Create High Level Architecture for AI System Design
• Define Data Flow and Processing Requirements
• Specify AI Model Types and Algorithmic Approaches
• Document Integration Points with Existing Systems
• Outline Deployment and Operational Considerations

Module 13: Define Success Criteria (eg, KPIs, Metrics)

• Establish Measurable Performance Indicators for AI Models
• Define Business Impact Metrics and Success Thresholds
• Create Technical Performance Benchmarks and Targets
• Develop User Satisfaction and Adoption Measurement Criteria
• Align Success Metrics with Organisational Objectives

Module 14: Support Business Case Creation

• Gather Financial Data and Projected Benefits for Business Case
• Collaborate with Finance Teams on Cost Estimates and Projections
• Develop Compelling Narratives for Executive Presentations
• Provide Technical Expertise for Business Case Validation
• Review and Refine Business Case Documentation

Module 15: Identify Project Resources (eg, People, Hardware, Contractors)

• Assess Skill Requirements for AI Project Team Composition
• Evaluate Hardware and Infrastructure Needs for Development and Deployment
• Identify Gaps Requiring External Contractors or Consultants
• Plan Resource Allocation and Timeline for Project Phases
• Coordinate with Procurement for Specialised AI Tools and Platforms

Domain III: Identify Data Needs

Module 16: Define Required Data

• Specify Data Types and Formats Needed for AI Model Training
• Determine Data Volume Requirements and Sampling Strategies
• Identify Temporal and Granularity Requirements for Data Collection
• Define Data Quality Standards and Acceptance Criteria
• Map Data Requirements to Business Objectives and Use Cases

Module 17: Identify SMEs

• Locate Domain Experts with Knowledge of Relevant Data Sources
• Engage Business Users Who Understand Data Context and Meaning
• Connect with Data Stewards and Data Governance Teams
• Identify Technical Experts Familiar with Data Systems and Structures
• Establish Communication Channels with Identified Subject Matter Experts

Module 18: Identify Data Sources and Locations

• Map Internal Databases and Data Warehouses Containing Relevant Information
• Explore External Data Sources and Third-Party Data Providers
• Assess Cloud Storage and Distributed Data Repositories
• Inventory Legacy Systems and Historical Data Archives
• Document Data Ownership and Access Permissions

Module 19: Coordinate AI Workspace and Infrastructure

• Provision Computing Resources for Data Processing and Model Training
• Establish Secure Development Environments for AI Teams
• Configure Data Storage and Backup Systems for Project Needs
• Set Up Collaboration Tools and Version Control Systems
• Ensure Compliance with Security and Governance Requirements

Module 20: Gather Required Data

• Execute Data Extraction from Identified Sources and Systems
• Coordinate Data Transfers and Migrations to AI Development Environments
• Implement Data Collection Processes for Ongoing Data Feeds
• Validate Data Completeness and Accuracy During Collection
• Establish Data Refresh and Update Procedures

Module 21: Check Data Privacy, Compliance, and Access

• Verify Data Usage Rights and Licensing Agreements
• Ensure Compliance with Data Protection Regulations and Policies
• Implement Access Controls and User Permissions for Data Resources
• Conduct Privacy Impact Assessments for Data Usage
• Document Data Lineage and Usage for Audit Purposes

Module 22: Oversee Data Evaluation

• Assess Data Quality Dimensions Including Accuracy, Completeness, and Consistency
• Analyse Data Distributions and Identify Potential Biases or Gaps
• Evaluate Data Freshness and Relevance for AI Model Training
• Review Data Schema and Structure for Modelling Compatibility
• Conduct Exploratory Data Analysis to Understand Data Characteristics

Module 23: Determine If Data Meets Solution Needs

• Compare Available Data Against Defined Requirements and Specifications
• Assess Data Sufficiency for Training Robust AI Models
• Identify Data Gaps and Develop Strategies for Addressing Deficiencies
• Validate Data Representativeness for Target Use Cases
• Make Go/No Go Decisions Based on Data Readiness Assessment

Module 24: Convey Data Understanding to Leadership

• Prepare Executive Summaries of Data Assessment Findings
• Create Visualisations and Reports to Communicate Data Insights
• Present Data Readiness Status and Recommendations to Stakeholders
• Translate Technical Data Concepts into Business Relevant Language
• Provide Regular Updates on Data Preparation Progress and Challenges

Domain IV: Manage AI Model Development and Evaluation 16%

Module 25: Oversee AI/ML Model Technique(s) (eg, Algorithm Selection)

• Research and Evaluate Appropriate Algorithms for Specific Use Cases
• Guide Selection Between Supervised, Unsupervised, and Reinforcement Learning Approaches
• Assess Trade Offs Between Model Complexity, Performance, and Interpretability
• Coordinate with Data Scientists on Model Architecture Decisions
• Review Algorithm Selection Criteria and Decision Documentation

Module 26: Oversee AI/ML Model QA/QC (eg, Configuration Management, Model Performance)

• Establish Model Testing Protocols and Quality Assurance Procedures
• Implement Configuration Management for Model Versions and Parameters
• Monitor Model Performance Metrics During Development and Testing
• Coordinate Peer Reviews and Technical Validation of Model Designs
• Ensure Adherence to Coding Standards and Best Practices

Module 27: Manage AI/ML Model Training

• Plan Training Schedules and Resource Allocation for Model Development
• Monitor Training Progress and Computational Resource Utilisation
• Coordinate Hyperparameter Tuning and Optimisation Activities
• Oversee Cross Validation and Model Selection Processes
• Manage Training Data Versioning and Experiment Tracking

Module 28: Manage Data Transformation to Conduct Data Preparation

• Oversee Data Cleaning and Preprocessing Workflows
• Coordinate Feature Engineering and Selection Activities
• Manage Data Normalisation and Standardisation Processes
• Supervise Data Augmentation and Synthetic Data Generation
• Ensure Data Transformation Reproducibility and Documentation

Module 29: Verify Data Quality for Go/No Go Decision to Conduct Data Preparation

• Conduct Final Data Quality Assessments Before Model Training
• Validate Data Preprocessing and Transformation Results
• Assess Data Representativeness and Potential Bias Issues
• Make Decisions on Data Readiness for Model Development
• Document Data Quality Findings and Recommendations

Module 30: Verify Model Ready for Operationalization Go/No Go Decision

• Evaluate Model Performance Against Established Success Criteria
• Assess Model Robustness and Generalisation Capabilities
• Review Deployment Readiness Including Infrastructure Requirements
• Validate Model Documentation and Operational Procedures
• Make Final Approval Decisions for Model Deployment

Domain V: Operationalize AI Solution

Module 31: Manage Creation of AI Solution Deployment Plan

• Develop Comprehensive Deployment Strategy and Timeline
• Plan Infrastructure Requirements and Resource Allocation
• Coordinate with IT Teams on System Integration and Deployment
• Establish Rollback Procedures and Contingency Plans
• Create Deployment Checklists and Validation Criteria

Module 32: Manage AI Solution Deployment

• Coordinate Deployment Activities Across Technical Teams
• Monitor Deployment Progress and Resolve Implementation Issues
• Validate System Functionality and Performance in Production Environment
• Manage User Access Provisioning and Security Configurations
• Conduct Post Deployment Verification and Testing

Module 33: Oversee Model Governance

• Establish Model Lifecycle Management Procedures
• Implement Model Versioning and Change Control Processes
• Monitor Model Performance and Drift Detection
• Coordinate Model Updates and Retraining Schedules
• Ensure Compliance with Governance Policies and Standards

Module 34: Oversee AI Solution Metrics (eg, KPI, Model Performance)

• Implement Monitoring Dashboards for Business and Technical Metrics
• Track Key Performance Indicators and Success Measures
• Analyse Model Performance Trends and Degradation Patterns
• Generate Regular Performance Reports for Stakeholders
• Establish Alerting Systems for Performance Threshold Breaches

Module 35: Prepare Final Report/Lessons Learned

• Document Project Outcomes and Achievement of Objectives
• Capture Lessons Learned and Best Practices for Future Projects
• Analyse What Worked Well and Areas for Improvement
• Create Knowledge Transfer Documentation for Operational Teams
• Present Final Project Results to Stakeholders and Leadership

Module 36: Manage AI Solution Transition Plan

• Plan Transition from Project Team to Operational Support
• Coordinate Knowledge Transfer to Production Support Teams
• Establish Ongoing Maintenance and Support Procedures
• Define Roles and Responsibilities for Operational Phase
• Create Handover Documentation and Training Materials

Module 37: Oversee AI Solution Contingency Plan

• Develop Incident Response Procedures for AI System Failures
• Plan Backup and Disaster Recovery Strategies
• Establish Escalation Procedures for Critical Issues
• Create Business Continuity Plans for AI Service Disruptions
• Test and Validate Contingency Procedures Regularly

Who Should Attend this PMI Certified Professional in Managing AI Course? 

This course is designed for individuals involved in planning, managing, governing, or delivering AI initiatives. It is also suitable for those working with business needs, data, risk, compliance, and AI solution deployment. This course can benefit: 

• Project Managers 
• AI Project Managers 
• Product Managers 
• Business Analysts 
• Data and AI Professionals 
• Risk and Compliance Professionals 
• IT Managers 
• Technology Consultants 

Prerequisites for the PMI Certified Professional in Managing AI Course 

There are no formal prerequisites for attending this PMI Certified Professional in Managing AI Course. However, a basic understanding of AI concepts, project management, or digital transformation can be beneficial.

PMI Certified Professional in Managing AI Course Overview 

The PMI Certified Professional in Managing AI Course introduces delegates to the key areas involved in managing AI initiatives. It covers responsible AI, business needs, data requirements, model development, deployment, governance, and performance monitoring. 

Delegates will develop skills in assessing AI feasibility, managing risks, defining project scope, and evaluating data readiness. The training also builds knowledge of model quality, deployment planning, governance, and solution performance. 

This 3-Day course by The Knowledge Academy enables delegates to manage AI initiatives across their lifecycle. They will be able to support responsible AI practices, coordinate data and model activities, oversee deployment, and monitor AI solution performance. 

PMI Certified Professional in Managing AI Course Objectives 

• To understand responsible and trustworthy AI governance practices 
• To assess AI feasibility, risks, scope, and business value 
• To identify data requirements, sources, quality, and readiness 
• To oversee AI model development, testing, and quality assurance 
• To manage AI solution deployment, governance, and performance 
• To support transition, reporting, and ongoing AI solution operations 

Learning Outcomes of this PMI Certified Professional in Managing AI Course 

Upon completing this PMI Certified Professional in Managing AI Course, delegates will be able to manage AI initiatives across key stages of the lifecycle. They will also be able to support governance, data readiness, model development, deployment, and ongoing solution performance.

What’s Included in this PMI Certified Professional in Managing AI Course? 

• World-Class Training Sessions from Experienced Instructors 
• Interactive Learning with 24/7 Support 
• Digital Delegate Pack