AI Product Team Training Paths

Organizations building AI products require specialized skills beyond traditional software development. This page defines role-based training paths that align with AEEF standards, maturity levels, and the AI product lifecycle.

For foundational training on AI-assisted software development (using AI tools to write code), see Training & Skill Development.

Training Path Overview

Role	Focus Area	AEEF Standards Emphasis	Target Maturity
ML Engineer	Model development, training, deployment	PRD-STD-010, 011, 012, 015	Level 3+
Data Scientist	Data analysis, feature engineering, evaluation	PRD-STD-011, 014, 015	Level 3+
AI Product Manager	Product strategy, safety, compliance	PRD-STD-010, 013, 014, 016	Level 2+
AI Safety Engineer	Safety evaluation, red-teaming, monitoring	PRD-STD-010, 015; Pillar 2 profiles	Level 3+
MLOps Engineer	Infrastructure, pipelines, observability	PRD-STD-007, 012; lifecycle guides	Level 3+

ML Engineer Training Path

Foundation (Level 2)

Module	Topics	Assessment
AEEF Standards Orientation	PRD-STD overview, RFC 2119 language, compliance levels	Quiz: identify MUST vs. SHOULD requirements
Model Development Standards	PRD-STD-011 requirements, model cards, data lineage documentation	Create a compliant model card for an existing model
Safety & Trust Controls	PRD-STD-010 requirements, safety evaluation suites, content filtering	Implement safety gates for a sample model
Inference Reliability	PRD-STD-012 requirements, SLOs, fallback strategies, cost controls	Design SLO dashboard for a production model

Intermediate (Level 3)

Module	Topics	Assessment
Multilingual Model Quality	PRD-STD-015 requirements, cross-language evaluation, dialect handling	Run multilingual evaluation suite, analyze parity gaps
Model Registry & Versioning	Registry workflows, semantic versioning for ML, promotion gates	Register and promote a model through staging to production
Experiment Design	A/B testing, canary deployment, statistical rigor	Design an experiment plan with hypothesis, MDE, sample size
Retraining Pipelines	Trigger criteria, feedback loops, continuous learning governance	Build a retraining pipeline with data validation gates

Advanced (Level 4-5)

Module	Topics	Assessment
Multi-Tenant Model Serving	PRD-STD-013, tenant-scoped configurations, isolation patterns	Implement tenant-scoped safety policies for a shared model
Fairness Engineering	Bias detection, mitigation strategies, fairness cards	Complete a fairness assessment for a production model
Advanced Safety	Red-teaming, adversarial evaluation, jailbreak resistance	Conduct a red-team exercise and document findings

Data Scientist Training Path

Foundation (Level 2)

Module	Topics	Assessment
Data Governance Standards	PRD-STD-011, data classification, rights management	Audit a training dataset for compliance
Privacy & Data Rights	PRD-STD-014, DPIA process, consent management, data subject rights	Complete a DPIA for a sample AI feature
Data Quality Fundamentals	Labeling standards, inter-annotator agreement, data validation	Evaluate labeling quality for an existing dataset

Intermediate (Level 3)

Module	Topics	Assessment
Training Data Governance	Sourcing requirements, versioning, lifecycle management	Create a data governance plan for a new training dataset
Cross-Border Data Handling	Transfer mechanisms, jurisdictional requirements (KSA, UAE, Egypt, EU)	Map data flows for a multi-region AI product
Evaluation Design	Evaluation set construction, metric selection, statistical validity	Design an evaluation framework for a production model

Advanced (Level 4-5)

Module	Topics	Assessment
Machine Unlearning	Deletion verification, approximate unlearning techniques	Implement and verify data deletion from a trained model
Multilingual Data Engineering	Cross-language data pipelines, dialect-aware preprocessing	Build a multilingual data pipeline with quality gates
Fairness Measurement	Metric selection, intersectional analysis, segment-level evaluation	Produce a fairness report with mitigation recommendations

AI Product Manager Training Path

Foundation (Level 2)

Module	Topics	Assessment
AEEF Framework Overview	Five pillars, maturity model, standards index, KPI framework	Map an existing AI product to AEEF maturity levels
AI Product Safety	PRD-STD-010, risk tiers, safety evaluation, rollout containment	Define risk tier and safety requirements for a new feature
Compliance Landscape	Regulatory profiles (KSA, UAE, Egypt, EU), compliance checklists	Identify applicable regulations for a product deployment

Intermediate (Level 3)

Module	Topics	Assessment
Multi-Tenant Product Strategy	PRD-STD-013, tenant SLA mapping, cost allocation, isolation tiers	Design a tenant governance model for a SaaS AI product
Privacy Product Requirements	PRD-STD-014, privacy-by-design, consent UX, automated decision rights	Write privacy requirements for an AI feature PRD
Channel Governance	PRD-STD-016, channel inventory, platform compliance, consistency	Create a channel governance plan for a multi-channel AI product

Advanced (Level 4-5)

Module	Topics	Assessment
AI Product Lifecycle Management	Full lifecycle from data governance to retraining loops	Create end-to-end lifecycle plan for a new AI product
Experimentation Strategy	A/B testing, canary deployment, metric selection, go/no-go decisions	Design an experimentation roadmap for quarterly model updates
Regulatory Strategy	Multi-jurisdiction deployment, audit readiness, incident response	Develop a regulatory compliance roadmap for 3 target markets

AI Safety Engineer Training Path

Foundation (Level 2)

Module	Topics	Assessment
Safety & Trust Controls	PRD-STD-010 deep dive, safety evaluation suites, incident response	Audit an existing AI product against PRD-STD-010
Security Risk Framework	Pillar 2 security controls, OWASP LLM Top 10	Map OWASP LLM risks to an existing AI product
Multilingual Safety	PRD-STD-015, cross-language safety testing, cultural sensitivity	Design a multilingual safety test suite

Intermediate (Level 3)

Module	Topics	Assessment
Red-Teaming Methodology	Structured red-teaming, adversarial prompt design, jailbreak testing	Conduct a red-team exercise and produce a findings report
Fairness & Bias Assessment	Bias detection, protected attributes, mitigation validation	Complete a fairness assessment using AEEF templates
Drift & Degradation Monitoring	Production monitoring, drift detection, alert design	Design a monitoring dashboard with safety-specific alerts

Advanced (Level 4-5)

Module	Topics	Assessment
Regulatory Safety Requirements	EU AI Act safety obligations, KSA/UAE safety alignment	Produce a regulatory safety compliance report
Advanced Adversarial Testing	Multi-turn attacks, tool-use exploitation, agent safety	Design and execute an advanced adversarial test campaign
Safety Culture Development	Incident post-mortems, safety review processes, team training	Develop a safety culture program for an AI product team

MLOps Engineer Training Path

Foundation (Level 2)

Module	Topics	Assessment
Quality Gates & CI/CD	PRD-STD-007, pipeline integration, gate configuration	Configure AEEF quality gates in a CI/CD pipeline
Inference Reliability	PRD-STD-012, SLO design, fallback strategies, cost controls	Implement SLO monitoring for a production inference service
Platform Integration	CI/CD platform patterns (GitHub, GitLab, Azure DevOps, Bitbucket)	Implement quality gates on a non-GitHub CI/CD platform

Intermediate (Level 3)

Module	Topics	Assessment
Model Registry Operations	Registry setup, versioning, artifact management, promotion workflows	Deploy and operate a model registry with promotion gates
Training Pipeline Automation	Continuous training, data validation, retraining governance	Build an automated retraining pipeline with approval gates
Production Monitoring	Drift detection, metric dashboards, alerting, incident response	Implement production monitoring for a deployed model

Advanced (Level 4-5)

Module	Topics	Assessment
Multi-Tenant Infrastructure	Tenant isolation patterns, resource allocation, cost tracking	Implement tenant-isolated model serving infrastructure
Canary Deployment Automation	Automated canary progression, auto-halt criteria, traffic splitting	Build an automated canary deployment pipeline
Observability at Scale	Distributed tracing for AI pipelines, cost attribution, capacity planning	Design an observability architecture for a multi-model platform

Assessment and Certification

Assessment Criteria

Each module concludes with a practical assessment. Passing criteria:

Maturity Level	Assessment Type	Passing Threshold
Level 2 (Foundation)	Quiz + documentation exercise	80% score
Level 3 (Intermediate)	Hands-on implementation task	Functional implementation meeting AEEF requirements
Level 4-5 (Advanced)	End-to-end project deliverable	Peer-reviewed deliverable approved by domain lead

Certification Levels

Certification	Requirements	Validity
AEEF AI Product Practitioner	Complete Foundation modules for one role path	2 years
AEEF AI Product Specialist	Complete Foundation + Intermediate modules	2 years
AEEF AI Product Expert	Complete all modules for one role path	1 year (requires recertification)

Continuous Learning

• Quarterly updates — training content SHOULD be reviewed and updated quarterly to reflect AEEF standard revisions and emerging best practices
• Community of Practice — organizations SHOULD establish AI product communities of practice for cross-role knowledge sharing
• Incident-based learning — post-incident reviews SHOULD identify training gaps and generate new training modules

Cross-References

• Training & Skill Development
• Maturity Model
• PRD-STD-010: AI Product Safety & Trust
• PRD-STD-011: Model & Data Governance
• PRD-STD-012: Inference Reliability & Cost Controls
• PRD-STD-013: Multi-Tenant AI Governance
• PRD-STD-014: AI Product Privacy & Data Rights
• PRD-STD-015: Multilingual AI Quality & Safety
• PRD-STD-016: Channel-Specific AI Governance
• Training Data Governance
• Model Registry & Versioning
• Fairness & Bias Assessment
• A/B Testing & Canary Deployment
• Retraining & Feedback Loops