Certified Professional in Managing AI™

I contenuti del corso sono completamente allineati alla metodologia CPMAI ufficiale del PMI.

Modulo 0 – Introduction to PMI-CPMAI (e-learning)

• Introduction
• Why learn AI Project Management
• How this course works

Modulo 1 – The need for AI Project Management (e-learning)

• Why AI now?
• The Seven Patterns of AI
• Why AI projects fail
• The layers of trustworthy AI
• Iterative and Agile Processes for AI
• Cognitive Project Management in AI

SESSIONE LIVE 1 – AI Foundations & Phase I (4 ore)

• Consolidamento concetti base (20-25% delle domande d’esame)
• I 7 Pattern dell’AI – cuore dell’esame
• Trustworthy AI – le 5 Layers
• Agile & Iterative Approaches per AI

Modulo 2 – CPMAI Phase I – Business Understanding (e-learning)

• Determine problems 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

Modulo 3 – CPMAI Phase II – Data Understanding (e-learning)

• 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

SESSIONE LIVE 2 – Phases I & II Recap (4 ore)

• Mapping corretto business need → AI pattern
• Identificazione requisiti dati: quantità, qualità, formati
• I 4 Vs del Big Data
• Data Governance, Privacy, Compliance
• Logica Go/No-Go tra Phase I → Phase II

Modulo 4 – CPMAI Phase III – Data Preparation (e-learning)

• Data preparation for AI projects
• Data pipeline in AI projects
• Data quality check and verification
• Data transformation and augmentation for AI
• Data labeling for AI
• Data management for generative AI systems
• Trustworthy AI in data preparation

Modulo 5 – CPMAI Phase IV – Model Development (e-learning)

• Machine learning and models
• Model development
• Model validation
• Building generative AI systems
• Mapping AI development to CPMAI Phase IV

SESSIONE LIVE 3 – Phases III & IV Recap (4 ore)

• Data Pipeline end-to-end: ETL, ELT, ingestion
• Data Preparation Techniques: cleaning, transformation, splitting, augmentation, labeling
• Data Management per GenAI
• Fondamenti Phase IV – Model Development
• Machine Learning Algorithms & Model Selection

Modulo 6 – CPMAI Phase V – Model Evaluation (e-learning)

• Model Evaluation
• Model Iteration
• Model Performance, data and model drift
• Evaluating models against business and technology KPIs
• AI system monitoring and management
• Explainable and interpretable AI systems

Modulo 7 – CPMAI Phase VI – Operationalizing AI (e-learning)

• Moving AI models into operation
• AI platforms and infrastructure
• Ways to interact with AI models
• Operationalizing generative AI
• Model life cycle management
• AI and model governance
• Trustworthy AI considerations in operation
• Limits of AI
• Moving to the next iteration after VI

SESSIONE LIVE 4 – Phase V & VI Recap + Exam Prep (4 ore)

• Phase V: metriche e tecniche di valutazione
• Error analysis e risk-based evaluation
• Continuous monitoring, drift e retraining
• Phase VI: MLOps, Governance, Monitoring & Security
• Strategie per l’esame: time management, elimination techniques
• Simulazione finale e Q&A