A tailored course, built for your situation
Mastering the AI Act for Senior Data Platform Practitioners
Turn compliance rigor into cross-functional influence
$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.
AI governance feels fragmented, teams use different rules, timelines, and playbooks, leading to delays and rework.
The situation this course is for
Even in mature data environments, AI governance often lives in silos: legal defines risk thresholds, ML teams ship models, and platform engineers maintain infrastructure, without a shared implementation standard. The AI Act introduces a common framework, but turning it into action requires someone who speaks both policy and platform engineering. That’s where practitioners like you step in, with technical authority and implementation fluency, to unify efforts.
Who this is for
Senior data platform engineer or architect with deep Databricks expertise, now expanding into AI governance and cross-team coordination
Who this is not for
Entry-level engineers, standalone data scientists without platform ownership, or legal-only compliance officers without technical implementation experience
What you walk away with
- Translate AI Act articles into technical controls for model logging, bias testing, and incident response
- Create reusable governance templates that regional teams adopt without rework
- Lead alignment sessions between legal, ML, and infrastructure teams using shared artifacts
- Position yourself as the primary contributor to your organization’s AI governance playbook
- Ship first internal AI Act compliance package ahead of regulatory review cycles
The 12 modules (with all 144 chapters)
Module 1. AI Act Foundations for Platform Engineers
Grounds the AI Act in technical implementation, focusing on obligations relevant to data infrastructure, model deployment, and audit trails.
12 chapters in this module
- Understanding the AI Act’s scope beyond classification
- How high-risk AI systems are defined in Article 6
- Mapping data pipeline stages to AI lifecycle phases
- Obligations for model transparency and documentation
- Distinguishing AI Act from GDPR and sector-specific rules
- Key timelines for compliance in EU and aligned markets
- Role of technical leads in governance sign-off
- How the AI Act impacts ML model versioning
- Understanding conformity assessments for internal use
- Vendor tools covered under the AI Act’s scope
- Incident reporting requirements for platform teams
- Connecting AI Act compliance to existing SOC 2 controls
Module 2. Translating Articles into Technical Controls
Converts legal language into engineering actions, focusing on logging, model data provenance, and human oversight mechanisms.
12 chapters in this module
- Turning Article 12 on data quality into pipeline checks
- Implementing logging for real-time model decisions
- Designing bias detection workflows for batch inference
- Building human-in-the-loop triggers for model output
- Securing model input data with audit trails
- Versioning model outputs for reproducibility
- Setting thresholds for automatic model alerts
- Integrating explainability tools into inference paths
- Validating model drift detection pipelines
- Creating rollback procedures for non-compliant models
- Documenting model assumptions for auditors
- Aligning logging with NIST AI RMF detection controls
Module 3. Governance Workflow Design for Multi-Team Alignment
Builds a cross-functional governance process that integrates data engineers, ML scientists, and compliance teams using shared templates.
12 chapters in this module
- Identifying governance handoffs in the model lifecycle
- Designing stage-gates for model certification
- Building shared dashboards for compliance tracking
- Creating playbooks for incident escalation paths
- Aligning model review cycles with audit timelines
- Standardizing model documentation across teams
- Using Databricks workflows to enforce governance steps
- Automating compliance checklist completion
- Integrating legal review into CI/CD pipelines
- Defining roles: who approves, who implements, who audits
- Managing version-controlled policy updates
- Reducing governance bottlenecks with parallel tracks
Module 4. Model Risk Classification and Tiering
Provides a practical system to classify models by risk level under the AI Act, enabling proportional controls.
12 chapters in this module
- Applying the EU’s high-risk use-case list to internal models
- Assessing model impact on rights and safety
- Developing internal risk scoring for non-listed models
- Tiering models: high-risk, limited-risk, minimal-risk
- Documenting classification rationale for auditors
- Involving legal and ethics committees in tiering
- Re-evaluating model risk after retraining
- Linking risk tier to testing and logging requirements
- Adjusting oversight based on deployment context
- Handling edge cases: hybrid and dual-use models
- Using risk tier to allocate audit resources
- Automating risk classification in model registration
Module 5. Bias Detection and Mitigation for Production Models
Implements Article 4’s data quality requirements with automated fairness checks and retraining triggers.
12 chapters in this module
- Defining bias metrics for different model types
- Sampling inference data for fairness testing
- Detecting proxy use in sensitive attributes
- Implementing demographic parity checks
- Monitoring for disparate impact over time
- Building feedback loops for user-reported bias
- Integrating bias testing into model validation
- Setting thresholds for model flagging
- Creating model cards with bias summary metrics
- Documenting mitigation actions for auditors
- Auditing bias testing for reproducibility
- Aligning bias checks with ISO 42001 fairness clauses
Module 6. Human Oversight Mechanisms in AI Systems
Designs practical human-in-the-loop workflows that satisfy Article 14 while maintaining operational efficiency.
12 chapters in this module
- Identifying when human review is mandatory
- Designing escalation paths for model decisions
- Building alerts for high-impact model outputs
- Training reviewers to interpret model rationale
- Logging human override actions for audit
- Balancing oversight with automation speed
- Using exception handling to reduce review load
- Setting time-to-review SLAs for critical models
- Documenting review protocols for regulators
- Integrating oversight into incident response
- Testing human review workflows quarterly
- Automating handoff to compliance teams
Module 7. Transparency and Documentation for Audits
Creates standardized, reusable documentation that satisfies Article 13 requirements and speeds up audits.
12 chapters in this module
- Structuring technical documentation for auditors
- Automating model metadata collection
- Generating model performance summaries
- Documenting training data lineage and cleaning
- Capturing model assumptions and limitations
- Building version-controlled model cards
- Linking documentation to deployment pipelines
- Using Databricks notebooks as evidence sources
- Maintaining audit trails for model updates
- Summarizing compliance status for non-technical leaders
- Creating executive-facing compliance dashboards
- Preparing for unannounced regulator inquiries
Module 8. Incident Response Planning for Non-Compliant AI
Builds a response protocol for AI incidents that meets Article 65 requirements and protects the organization.
12 chapters in this module
- Defining what constitutes an AI incident
- Creating incident classification tiers
- Building internal reporting workflows
- Notifying authorities within required timelines
- Preserving model and data state for investigation
- Conducting root-cause analysis for AI failures
- Updating models after incident resolution
- Logging incident resolution for auditors
- Communicating externally when required
- Integrating AI incidents into broader security ops
- Running tabletop exercises for incident readiness
- Documenting response improvements quarterly
Module 9. Vendor and Third-Party AI Risk Management
Extends AI Act compliance to third-party models and tools used in the data stack.
12 chapters in this module
- Assessing vendor AI tools for high-risk classification
- Reviewing vendor conformity assessments
- Contractual clauses for AI compliance obligations
- Auditing vendor model documentation
- Monitoring third-party model updates for compliance
- Managing open-source AI tool risks
- Requiring vendor incident reporting
- Conducting due diligence on AI API providers
- Handling non-compliant vendor models
- Establishing vendor review cycles
- Creating vendor scorecards for compliance
- Integrating vendor risk into internal dashboards
Module 10. Cross-Regional Compliance Alignment
Maps AI Act requirements to other jurisdictions like US AI directives and ISO 42001 to reduce duplication.
12 chapters in this module
- Comparing AI Act with NIST AI RMF guidelines
- Aligning with ISO 42001 governance structure
- Mapping controls to state-level US AI laws
- Harmonizing with UK AI white paper principles
- Leveraging AI Act work for Canadian AIDA
- Building a unified compliance framework
- Documenting compliance gaps by region
- Prioritizing controls with highest overlap
- Creating regional exception logs
- Training global teams on core principles
- Using one audit package for multiple regions
- Reducing rework through modular templates
Module 11. Internal Training and Adoption Strategy
Equips you to train engineers and data scientists on AI Act practices using role-specific materials.
12 chapters in this module
- Creating role-based training modules
- Designing hands-on labs for engineers
- Developing executive briefings on compliance
- Running cross-functional workshops
- Measuring training effectiveness
- Using Databricks workflows as teaching tools
- Gamifying compliance learning
- Creating on-demand resources
- Onboarding new team members
- Updating training after regulation changes
- Tracking team certification status
- Linking training to model approval gates
Module 12. Sustaining Compliance at Scale
Implements automated monitoring, version control, and governance review cycles to maintain long-term compliance.
12 chapters in this module
- Automating compliance checks in CI/CD
- Creating model registry governance rules
- Versioning compliance artifacts
- Scheduling recurring model audits
- Tracking compliance debt
- Integrating with security information systems
- Generating compliance health reports
- Monitoring for regulatory updates
- Updating playbooks after audits
- Conducting annual governance reviews
- Optimizing workflows for efficiency
- Scaling governance to new business units
How this maps to your situation
- Preparing for regulatory scrutiny
- Leading cross-functional AI alignment
- Reducing rework across teams
- Positioning as a governance leader
Before vs. after
Before
AI governance efforts are fragmented across teams, with inconsistent implementation, redundant work, and delayed compliance timelines.
After
You lead a unified, reusable governance process that scales across engineering and compliance teams, reducing rework and expanding your influence.
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: 90 minutes of focused learning, designed to be completed in one session or across multiple sittings.
If nothing changes
Without a structured approach, AI governance remains reactive, leading to last-minute scrambles during audits, inconsistent enforcement, and missed opportunities to lead cross-organizational initiatives.
How this compares to the alternatives
Unlike generic AI ethics courses, this course delivers specific, actionable steps aligned with the AI Act’s legal requirements and technical implementation patterns used by leading data organizations.
Frequently asked
Is this course technical or policy-focused?
It’s designed for technical practitioners who need to implement policy. You’ll learn how to translate legal requirements into engineering controls, documentation, and automated workflows.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead cross-team initiatives?
Yes. The course includes governance workflow design and alignment strategies used by practitioners who coordinate between data engineering, ML, and compliance teams.
$199 one-time. 90 minutes of focused learning, designed to be completed in one session or across multiple sittings..
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