A tailored course, built for your situation
Mastering AI Act for Data and AI Practitioners
Turn emerging AI compliance requirements into completed implementation artefacts in half the time
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Spending too long moving from AI policy drafts to working compliance outputs
The situation this course is for
Teams are drowning in guidance but starved for execution paths. The AI Act introduces broad obligations, but very few practitioners have a clear, repeatable way to convert those into risk assessments, documentation packages, or audit-ready artefacts, especially under tight cycles.
Who this is for
Senior data and AI practitioners in regulated environments who must translate compliance mandates into technical implementation
Who this is not for
Entry-level analysts, consultants without implementation experience, or those seeking board-level narratives without technical grounding
What you walk away with
- Produce AI Act-compliant risk classification matrices in under 90 minutes
- Generate fully traceable data lineage documentation aligned with Article 10 requirements
- Complete model transparency packages with version control and bias assessment checkpoints
- Deploy a modular evidence framework that passes internal legal review on first submission
- Move from policy update to artefact completion 2x faster than current team benchmarks
The 12 modules (with all 144 chapters)
Module 1. Understanding AI Act Scope and High-Risk Categories
Map the AI Act’s definition of high-risk systems to your current data pipeline and model inventory with precision. Identify which systems fall under Title III and require immediate action.
12 chapters in this module
- Defining artificial intelligence under the AI Act proposal
- Identifying high-risk AI systems in data-intensive environments
- Mapping Article 6 criteria to existing machine learning workflows
- Distinguishing general-purpose AI from domain-specific models
- Using the EBA’s draft guidelines to classify risk tiers
- Aligning NIST AI RMF categories with AI Act classifications
- Documenting system purpose to avoid over-scope creep
- Leveraging Unity Catalog metadata for initial screening
- Engaging legal teams with clear boundary definitions
- Creating a living register of AI system classifications
- Updating classification as models evolve post-deployment
- Avoiding false positives in automated risk flagging
Module 2. Data Provenance and Quality Requirements Under Article 10
Build traceable, auditable data pipelines that satisfy AI Act data quality obligations. Focus on lineage, representativeness, and bias mitigation from source to training set.
12 chapters in this module
- Understanding Article 10’s data quality mandates for training sets
- Establishing minimum standards for representativeness and bias
- Using Delta Lake versioning for immutable data tracking
- Integrating data quality checks into CI/CD pipelines
- Mapping upstream data sources to model inputs
- Documenting data cleaning and transformation logic
- Generating automated lineage reports from Unity Catalog
- Ensuring geographic and demographic coverage in training data
- Validating data relevance to intended model use case
- Creating audit-ready data provenance packages
- Handling synthetic data under AI Act transparency rules
- Versioning datasets alongside model iterations
Module 3. Technical Documentation for High-Risk Models
Assemble technical documentation that meets AI Act Annex IV requirements, including design choices, training data, and performance metrics, all in a reusable format.
12 chapters in this module
- Structuring documentation per Annex IV section 1
- Detailing system architecture and dependencies
- Documenting training data composition and sources
- Recording hyperparameters and training pipeline steps
- Capturing model performance across subgroups
- Including instructions for proper use and limitations
- Formatting logs for automated compliance checks
- Using Markdown templates for consistent submissions
- Linking documentation to model registry entries
- Automating documentation updates on retraining
- Versioning docs with Git and Databricks Repos
- Preparing documentation for external auditor access
Module 4. Risk Management System Design for AI Act Compliance
Implement a continuous risk management process aligned with Article 9, from identification to mitigation and documentation, tailored to AI system lifecycles.
12 chapters in this module
- Establishing a risk identification protocol for AI systems
- Classifying risks by severity and likelihood
- Creating risk treatment plans for high-severity issues
- Integrating risk assessments into sprint planning
- Setting thresholds for unacceptable risk exposure
- Documenting rationale for residual risk acceptance
- Updating risk profiles after model updates
- Conducting retrospectives on risk detection gaps
- Linking risk logs to incident response procedures
- Using automated tools for real-time risk monitoring
- Ensuring third-party model risks are assessed
- Aligning with ISO 31000 risk management principles
Module 5. Human Oversight Mechanisms and Deployment Controls
Design human-in-the-loop processes that fulfill AI Act requirements for meaningful oversight, including override capabilities and monitoring thresholds.
12 chapters in this module
- Defining meaningful human oversight per Article 14
- Designing role-based access for intervention points
- Setting alerts for model drift and anomaly detection
- Creating escalation paths for critical decisions
- Building user interfaces for override execution
- Logging all human interventions for audit trail
- Training staff on oversight responsibilities
- Balancing automation speed with control requirements
- Ensuring oversight works in real-time scenarios
- Testing override mechanisms under load
- Documenting oversight procedures for legal review
- Updating protocols after incident reviews
Module 6. Transparency and User Communication Requirements
Meet AI Act transparency obligations by crafting clear, accessible information for users, including model purpose, limitations, and interaction logs.
12 chapters in this module
- Drafting user-facing summaries of AI system purpose
- Disclosing automated decision-making to end users
- Providing meaningful information about model logic
- Ensuring accessibility of transparency materials
- Logging user interactions for audit readiness
- Handling user requests to review automated decisions
- Creating multilingual disclosure templates
- Integrating transparency into product onboarding
- Securing logs of user notifications and consents
- Updating disclosures when model scope changes
- Validating clarity through user testing
- Aligning with GDPR and AI Act joint obligations
Module 7. Bias Detection and Mitigation Workflows
Implement proactive bias detection across model development and deployment cycles, fulfilling AI Act fairness requirements with technical rigor.
12 chapters in this module
- Defining fairness metrics for specific use cases
- Using SHAP and LIME for explainability in production
- Auditing training data for demographic imbalances
- Testing model outputs across protected attributes
- Setting thresholds for acceptable disparity
- Implementing bias mitigation techniques pre-deployment
- Monitoring live models for emerging disparities
- Creating bias incident response playbooks
- Documenting mitigation efforts for auditors
- Engaging external experts for validation
- Updating fairness checks after data refreshes
- Balancing accuracy and fairness trade-offs
Module 8. Cybersecurity and Robustness for AI Systems
Strengthen model resilience against adversarial attacks and ensure robustness under stress conditions as required by Article 15.
12 chapters in this module
- Applying NIST CSF controls to AI infrastructure
- Testing models against evasion and poisoning attacks
- Implementing input validation and sanitization
- Monitoring for anomalous prediction patterns
- Setting up automated rollback on integrity failure
- Securing model weights and checkpoints
- Enforcing strict access controls on APIs
- Conducting penetration testing on AI endpoints
- Building redundancy into critical AI services
- Validating robustness under edge-case inputs
- Documenting security testing results
- Integrating findings into DevSecOps pipelines
Module 9. Record Keeping and Audit Trail Requirements
Automate the generation of audit-compliant records, including model version history, training runs, and deployment decisions.
12 chapters in this module
- Identifying mandatory records under Article 18
- Configuring automatic logging for model lifecycle
- Storing records in tamper-proof formats
- Ensuring retention periods meet regulatory standards
- Indexing logs for fast regulator queries
- Linking records to specific AI Act articles
- Exporting audit packages in standard formats
- Integrating with existing SIEM and logging tools
- Redacting sensitive data without breaking traceability
- Testing recovery of historical records
- Aligning with SOC 2 logging standards
- Preparing for unannounced regulator requests
Module 10. Third-Party and Open Source Model Governance
Extend AI Act compliance to vendor-supplied and open-source models with due diligence, integration checks, and ongoing monitoring.
12 chapters in this module
- Assessing third-party AI vendor compliance posture
- Reviewing model documentation for completeness
- Testing external models for bias and drift
- Integrating third-party models into internal logging
- Establishing contractual obligations for updates
- Conducting security reviews of open-source models
- Tracking license compliance for public models
- Creating deployment checklists for external AI
- Monitoring vendor support and patch cycles
- Documenting risk acceptance for legacy models
- Building fallback mechanisms for discontinued models
- Auditing third-party model performance independently
Module 11. Internal Audit and Compliance Verification Process
Run internal validation exercises that mirror regulator expectations, ensuring readiness for official reviews.
12 chapters in this module
- Designing audit scenarios based on AI Act focus areas
- Simulating regulator document requests
- Validating completeness of technical documentation
- Testing ability to reproduce model training runs
- Reviewing bias mitigation evidence packages
- Checking human oversight logs for gaps
- Verifying data provenance for recent models
- Assessing incident response playbooks
- Conducting tabletop exercises with legal teams
- Generating internal audit findings memos
- Prioritizing fixes based on risk exposure
- Reporting results to compliance steering committees
Module 12. Scaling Compliance Across AI Development Teams
Institutionalize AI Act readiness through reusable templates, automation, and cross-team enablement.
12 chapters in this module
- Creating standardized onboarding for new projects
- Building shared libraries of compliant patterns
- Automating AI Act checklist completion
- Integrating compliance gates into CI/CD
- Training engineers on documentation standards
- Establishing compliance champions in each pod
- Maintaining a central registry of all AI systems
- Generating executive summaries from artefacts
- Reducing review cycles through pre-validation
- Enabling self-service compliance for developers
- Updating playbooks as regulations evolve
- Measuring compliance velocity across teams
How this maps to your situation
- After the AI Act finalizes
- During first internal compliance audit
- Before model deployment to production
- When onboarding third-party AI systems
Before vs. after
Before
Waiting weeks to compile compliant AI governance outputs from fragmented sources and tribal knowledge
After
Producing AI Act-compliant artefacts in hours with structured, reusable workflows
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3-4 hours per module, designed to be completed in parallel with ongoing work.
If nothing changes
Organizations that delay implementation will face extended review cycles, regulatory scrutiny, and operational bottlenecks as enforcement ramps up.
How this compares to the alternatives
Generic AI ethics courses offer principles without execution. This course delivers the how, structured, repeatable, and tailored to AI Act’s specific obligations.
Frequently asked
Is this course focused on European Union implementation only?
While the AI Act is EU legislation, its influence is global. The methods apply to any organization deploying high-risk AI systems, especially those operating across jurisdictions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this course help me prepare for audits?
Yes. Every module builds toward creating auditable, regulator-ready artefacts that pass review the first time.
$199 one-time. Approximately 3-4 hours per module, designed to be completed in parallel with ongoing work..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours