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AIG8977 Mastering AI Act for Data Science Practitioners

$199.00
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A tailored course, built for your situation

Mastering AI Act for Data Science Practitioners

Build authoritative, compliant AI systems with command of the EU's foundational regulation

$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.

Who this is for

Senior data scientist or AI engineer working in regulated environments or building customer-facing AI systems subject to governance frameworks.

Who this is not for

This course is not for entry-level data analysts, software developers without AI responsibilities, or compliance staff without technical AI implementation experience.

What you walk away with

  • Map AI Act requirements directly to data science project phases and model lifecycle stages
  • Classify AI systems by risk level using official criteria and document justification
  • Design transparency documentation that satisfies Article 13 requirements for high-risk systems
  • Integrate conformity assessments into MLOps pipelines without slowing innovation
  • Lead internal reviews with confidence using precise regulatory language and precedent

The 12 modules (with all 144 chapters)

Module 1. Orientation to the AI Act and Its Implications for Data Science
Establish foundational knowledge of the AI Act’s scope, structure, and relevance to data science roles. Understand how the regulation defines AI and which models fall under high-risk categories.
12 chapters in this module
  1. Understanding the EU AI Act legislative timeline and current status
  2. Identifying high-risk AI systems under Annex III of the AI Act
  3. Distinguishing between prohibited and permitted AI practices
  4. How data science outputs intersect with AI Act compliance
  5. Regulatory definitions of 'training data', 'model drift', and 'output'
  6. Mapping data science workflows to AI Act obligations
  7. Key roles: Provider, Deployer, Distributor under Title III
  8. Obligations specific to open-source AI model publishing
  9. Transparency requirements for general-purpose AI models
  10. Record-keeping expectations for model development cycles
  11. Geographic scope: when the AI Act applies to non-EU organizations
  12. Common misinterpretations of model risk levels in practice
Module 2. Risk Classification Framework for AI Models
Learn to systematically assess and document model risk levels based on use case, impact, and context. Build defensible classifications aligned with Annex III criteria.
12 chapters in this module
  1. Step-by-step process for determining AI system risk tier
  2. Use cases triggering high-risk classification under Annex III
  3. Evaluating indirect harm potential in model deployment
  4. Documenting risk rationale for internal and external review
  5. Handling edge cases: low-risk models with high-stakes applications
  6. Versioning risk assessments across model updates
  7. Integrating risk classification into model cards
  8. Collaborating with legal teams on borderline classifications
  9. Maintaining consistency across multi-model systems
  10. Auditor expectations for risk documentation completeness
  11. Common pitfalls in misclassifying foundation model derivatives
  12. Updating classifications when deployment context changes
Module 3. Technical Documentation and Model Transparency
Develop comprehensive technical documentation that meets AI Act Article 13 requirements, enhancing reproducibility and audit readiness.
12 chapters in this module
  1. Structure of AI Act-compliant technical documentation
  2. Required content for model design and development process
  3. Documenting dataset provenance and preprocessing steps
  4. Model architecture descriptions for non-technical reviewers
  5. Version control practices for model and documentation
  6. Creating accessible summaries for end-users
  7. Best practices for logging model behavior in production
  8. Including bias evaluation results in documentation
  9. Meeting traceability requirements across system components
  10. Using standardized templates without sacrificing detail
  11. Linking documentation to conformity assessment reports
  12. Updating documentation for model retraining events
Module 4. Data Governance Requirements Under the AI Act
Implement data management practices that satisfy AI Act data quality and provenance mandates, ensuring model inputs are lawful and representative.
12 chapters in this module
  1. Legal basis for training data collection under GDPR and AI Act
  2. Assessing representativeness and bias in training datasets
  3. Documentation of data preprocessing and feature engineering
  4. Versioning datasets for reproducibility and audit
  5. Handling synthetic data generation under AI Act scrutiny
  6. Provenance tracking from source to model input
  7. Data retention policies aligned with regulatory timelines
  8. Labeling quality standards for supervised learning tasks
  9. Third-party data sourcing and due diligence checks
  10. Bias mitigation strategies during data preparation
  11. Audit trails for dataset modifications and updates
  12. Cross-border data transfer considerations for training
Module 5. Conformity Assessments for High-Risk AI Systems
Execute internal conformity assessments that demonstrate compliance with AI Act requirements, preparing for potential third-party audits.
12 chapters in this module
  1. Overview of conformity assessment routes under Title V
  2. Preparing for self-certification under Annex VI
  3. Gathering evidence for robustness, accuracy, and cybersecurity
  4. Documenting human oversight mechanisms
  5. Testing for adversarial robustness in real-world conditions
  6. Accuracy benchmarks tailored to intended use
  7. Cybersecurity measures for model and data protection
  8. Version control integration with conformity reporting
  9. Maintaining up-to-date conformity documentation
  10. External audit preparation strategies
  11. Common gaps found in initial conformity attempts
  12. Updating assessments for model updates and retraining
Module 6. Human Oversight and Explainability Implementation
Design effective human-in-the-loop controls and model explainability practices that meet AI Act requirements for high-risk systems.
12 chapters in this module
  1. Defining meaningful human oversight for automated decisions
  2. Role design for human reviewers in AI decision chains
  3. Timing and access requirements for human intervention
  4. Explainability methods appropriate to model type and use case
  5. Local vs. global interpretability trade-offs
  6. User-facing explanations under Article 13(3)
  7. Logging oversight actions for audit purposes
  8. Training programs for human reviewers
  9. Performance metrics for oversight effectiveness
  10. Integrating feedback loops from human reviewers
  11. Balancing explainability with performance constraints
  12. Documentation of oversight design choices
Module 7. Monitoring and Maintenance of Deployed AI Systems
Establish continuous monitoring systems that detect model degradation, drift, and compliance deviations in production environments.
12 chapters in this module
  1. Real-time performance tracking for high-risk models
  2. Statistical methods for detecting concept and data drift
  3. Logging model inputs, outputs, and decision contexts
  4. Automated alerts for degradation thresholds
  5. Scheduled re-evaluation cycles for long-running models
  6. Incident response protocols for non-compliant behavior
  7. Version rollback procedures for failed updates
  8. Maintaining human oversight availability post-deployment
  9. Updating risk assessments based on operational data
  10. Documentation of model monitoring activities
  11. Integration with existing MLOps tooling
  12. End-of-life planning for retired AI systems
Module 8. Third-Party AI and Vendor Management
Evaluate and manage third-party AI components and vendors in compliance with AI Act supply chain obligations.
12 chapters in this module
  1. Assessing vendor compliance with AI Act requirements
  2. Due diligence for open-source foundation models
  3. Contractual clauses for AI Act compliance assurance
  4. Right-to-audit provisions in vendor agreements
  5. Evaluating model cards and technical documentation quality
  6. Managing dependencies on non-compliant systems
  7. Liability allocation for high-risk AI components
  8. Vendor risk classification framework
  9. Ongoing monitoring of third-party AI updates
  10. Documentation of vendor assessment decisions
  11. Handling proprietary models with limited transparency
  12. Exit strategies for non-compliant vendors
Module 9. Record Keeping and Audit Readiness
Organize and maintain records that demonstrate continuous compliance with AI Act requirements for potential audits.
12 chapters in this module
  1. Required retention periods for AI system records
  2. Centralizing documentation across model lifecycle
  3. Versioned storage of model code and configurations
  4. Secure access controls for compliance records
  5. Preparing for unannounced regulatory inspections
  6. Indexing records for rapid retrieval
  7. Cross-referencing documentation with conformity reports
  8. Handling records in multi-jurisdictional deployments
  9. Data protection considerations for audit logs
  10. Training teams on documentation standards
  11. Automating record generation from CI/CD pipelines
  12. Audit simulation exercises for compliance readiness
Module 10. Internal Governance and Cross-Functional Collaboration
Lead cross-functional initiatives that align data science, legal, and compliance teams around AI Act implementation.
12 chapters in this module
  1. Establishing AI governance committees
  2. Defining roles for data scientists in compliance processes
  3. Creating standardized review workflows
  4. Facilitating communication between technical and legal teams
  5. Developing internal compliance checklists
  6. Integrating AI Act requirements into project intake
  7. Training non-technical stakeholders on AI risks
  8. Escalation paths for compliance disagreements
  9. Metrics for tracking organizational compliance maturity
  10. Sharing best practices across teams
  11. Managing conflicting priorities in agile environments
  12. Documenting governance decisions over time
Module 11. Preparing for Market Surveillance and Enforcement
Anticipate regulatory scrutiny and enforcement actions related to AI Act non-compliance, and prepare appropriate responses.
12 chapters in this module
  1. Understanding national market surveillance authorities
  2. Responding to information requests from regulators
  3. Preparing for on-site inspections
  4. Corrective action plans for identified deficiencies
  5. Voluntary disclosure procedures
  6. Managing public communications during investigations
  7. Legal protections for internal compliance reviews
  8. Coordinating with external counsel
  9. Lessons from early enforcement cases
  10. Updating policies after regulatory guidance
  11. Engaging with industry associations on enforcement trends
  12. Building organizational resilience to regulatory scrutiny
Module 12. Future-Proofing AI Strategy Under Evolving Regulation
Adapt data science strategy to anticipate future AI regulations and standards beyond the current AI Act framework.
12 chapters in this module
  1. Tracking proposed amendments to the AI Act
  2. Monitoring international AI regulation developments
  3. Aligning with ISO 42001 and NIST AI RMF standards
  4. Building modular compliance architectures
  5. Scenario planning for stricter enforcement
  6. Investing in proactive compliance capabilities
  7. Leveraging compliance as a competitive advantage
  8. Anticipating sector-specific AI rules in finance and health
  9. Engaging in policy discussions as a technical expert
  10. Training next-generation practitioners on AI ethics
  11. Measuring ROI of compliance investments
  12. Strategic roadmap for sustained AI governance leadership

How this maps to your situation

  • Model development lifecycle
  • Regulatory audit readiness
  • Cross-functional governance
  • Production monitoring and maintenance

Before vs. after

Before
Working reactively on AI compliance, relying on fragmented guidance and piecing together regulatory requirements during audits or reviews.
After
Leading proactively with a complete, structured command of the AI Act, able to design compliant systems from inception and speak authoritatively across teams.

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 8, 10 hours of focused learning, designed to be completed across two weeks with practical application between modules.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level policy summaries, this course delivers actionable, technical implementation guidance tied directly to the AI Act's legal text and enforcement expectations. It bridges the gap between legal requirements and data science execution, offering tools and frameworks not available in public documentation or academic settings.

Frequently asked

Is this course suitable for non-EU residents?
Yes. The AI Act sets a global benchmark for AI regulation, and its principles are being adopted by other jurisdictions. Compliance with its framework prepares organizations for future regulations worldwide.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course replace legal advice?
No. This course provides technical implementation guidance but does not offer legal counsel. Always consult qualified legal professionals for binding interpretations.
$199 one-time. Approximately 8, 10 hours of focused learning, designed to be completed across two weeks with practical application between modules..

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
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