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AIG9350 Mastering AI Act Compliance for Senior Data Platform Leaders

$199.00
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A tailored course, built for your situation

Mastering AI Act Compliance for Senior Data Platform Leaders

A structured path to defensible, repeatable AI governance that aligns with evolving regulatory expectations

$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 audit packages that require last-minute rework due to inconsistent lineage or schema volatility

Who this is for

Senior technical leader in data and AI platform infrastructure at a major cloud or enterprise software company, responsible for system design decisions that impact compliance outcomes

Who this is not for

Individuals focused solely on model development without platform ownership, junior compliance analysts, or those not involved in data architecture decisions

What you walk away with

  • Produce AI compliance documentation that passes internal and external review the first time
  • Trace data lineage from storage layer to model inference with documented rigor
  • Reduce rework cycles in audit deliverables by standardizing evidence collection
  • Align data platform evolution with AI Act requirements for transparency and accountability
  • Embed governance checks directly into data pipeline design workflows

The 12 modules (with all 144 chapters)

Module 1. Understanding the AI Act’s Data Provisions
Break down the specific requirements in the AI Act that apply to data storage, access, and lineage in high-risk AI systems.
12 chapters in this module
  1. Mapping AI Act Articles to data infrastructure responsibilities
  2. Identifying high-risk AI use cases in platform telemetry
  3. Regulatory definition of 'training data' vs operational data
  4. Traceability mandates from dataset to model output
  5. Documentation thresholds for data origin and preprocessing
  6. Understanding conformity assessments for data pipelines
  7. Role of technical documentation in audit readiness
  8. How data quality reports support AI Act compliance
  9. Versioning requirements for datasets and schemas
  10. Retention periods for model input and output data
  11. Data governance vs AI governance: where they intersect
  12. Common misconceptions about data scope in the AI Act
Module 2. Data Lineage for Audit-Ready Systems
Design lineage capture that survives schema evolution and platform scaling, ensuring continuous compliance.
12 chapters in this module
  1. Automated lineage extraction from Delta Lake metadata
  2. Capturing transformations across ETL and feature engineering
  3. Schema change impact tracking over model lifecycle
  4. Linking Parquet row groups to model training batches
  5. Storing lineage in queryable, human-readable formats
  6. Validating end-to-end traceability for spot audits
  7. Integrating lineage with Unity Catalog permissions
  8. Detecting gaps in lineage coverage during CI/CD
  9. Using tags and annotations to enrich lineage context
  10. Lineage retention vs storage cost tradeoffs
  11. Cross-system lineage when ingesting third-party data
  12. Making lineage actionable for non-technical reviewers
Module 3. Storage Layout and Model Accountability
Align columnar formats and partitioning strategies with AI Act transparency requirements.
12 chapters in this module
  1. How Parquet row group structure supports audit sampling
  2. Column-level statistics for bias detection workflows
  3. Compression artifacts and their impact on data integrity
  4. Partitioning strategies that preserve temporal lineage
  5. Efficient column pruning under regulatory access requests
  6. Storing provenance metadata in Parquet file footers
  7. Schema evolution patterns that break model reproducibility
  8. Balancing scan performance with audit granularity
  9. Lance vs Parquet: compatibility with AI documentation standards
  10. Detecting data drift at the page level in column stores
  11. Immutable storage layers for model evidence preservation
  12. Indexing strategies to accelerate compliance queries
Module 4. Schema Governance in Dynamic Environments
Maintain compliance across evolving schemas without sacrificing agility.
12 chapters in this module
  1. Schema change approval workflows for regulated pipelines
  2. Automated impact analysis on downstream models
  3. Versioned schema registry integration with CI/CD
  4. Detecting silent data type conversions in ETL
  5. Schema compatibility rules for backward/forward support
  6. Audit logging for schema modification events
  7. Schema documentation as part of technical files
  8. Handling nullable fields in high-risk AI contexts
  9. Enforcing data type constraints at ingestion
  10. Schema snapshots per model training cycle
  11. Cross-team schema change coordination protocols
  12. Reconciling schema drift with model validation records
Module 5. Data Quality as a Compliance Asset
Turn data quality monitoring into documented evidence for regulators.
12 chapters in this module
  1. Data quality metrics required by AI Act Annex III
  2. Automated profiling of training dataset distributions
  3. Detecting data leakage in time-based partitions
  4. Tracking completeness and consistency over time
  5. Setting thresholds for allowable data drift
  6. Reporting data quality in model technical documentation
  7. Integrating Great Expectations with pipeline monitoring
  8. Using data tests to support bias mitigation claims
  9. Anomaly detection in streaming data for AI systems
  10. Documenting data cleaning steps without obfuscation
  11. Data quality dashboards for regulator review
  12. Versioning data quality rules across model lifecycle
Module 6. Access Control and Audit Evidence
Design permission structures that produce clear, defensible access logs.
12 chapters in this module
  1. RBAC design for AI development vs production pipelines
  2. Attribute-based access control for sensitive datasets
  3. Logging all data access attempts for audit trails
  4. Masking vs filtering in compliance reporting queries
  5. Time-bound access for third-party model auditors
  6. Integrating with SSO and identity providers
  7. Documenting access policies in technical files
  8. Detecting privilege creep in long-lived service accounts
  9. Segregation of duties in model deployment workflows
  10. Audit log retention aligned with AI Act requirements
  11. Query logging without performance degradation
  12. Redacting PII in logs while preserving investigability
Module 7. Versioning Data and Models Together
Ensure reproducibility by tightly coupling data and model versions.
12 chapters in this module
  1. Tagging datasets used in specific model training runs
  2. Using Git-like versioning for large-scale datasets
  3. Storing dataset checksums in model metadata
  4. Automated capture of training environment state
  5. Versioning feature stores across experimentation
  6. Replaying training jobs from versioned data
  7. Data snapshots vs symbolic links for efficiency
  8. Cross-referencing data versions in model cards
  9. Version retention policies for audit compliance
  10. Handling large data updates without full retraining
  11. Version lineage from dev to production deployment
  12. Detecting data staleness in long-running models
Module 8. Technical Documentation for Regulators
Build living technical files that meet AI Act documentation standards.
12 chapters in this module
  1. Structure of the required technical documentation
  2. Data provenance section for regulator review
  3. Model development lifecycle narrative
  4. Risk management measures implementation details
  5. Including data quality monitoring results
  6. System architecture diagrams for audit clarity
  7. Version history tracking for compliance
  8. Update process for documentation changes
  9. Linking documentation to code repositories
  10. Using Markdown and automation for consistency
  11. Review cycles with legal and compliance teams
  12. Preparing documentation packages for notified bodies
Module 9. Automating Compliance Evidence Collection
Shift from manual deliverables to automated, continuous compliance.
12 chapters in this module
  1. Identifying recurring evidence requirements in AI Act
  2. Automated extraction of lineage and schema data
  3. Scheduling compliance snapshot jobs
  4. Validating evidence completeness before review
  5. Integrating with Jira and audit management tools
  6. Using Databricks Workflows for evidence pipelines
  7. Automated data quality reporting pipelines
  8. Generating model cards from metadata
  9. Building compliance dashboards for leadership
  10. Alerting on compliance threshold breaches
  11. Versioning evidence artifacts for traceability
  12. Secure storage and access for audit packages
Module 10. Preparation for Conformity Assessments
Align internal processes with formal AI Act evaluation procedures.
12 chapters in this module
  1. Internal audit checklists for high-risk AI systems
  2. Mock assessments with cross-functional teams
  3. Documenting risk management measures implementation
  4. Preparing technical leads for regulator interviews
  5. Gathering evidence for third-party notified bodies
  6. Responding to information requests during review
  7. Tracking open items from prior assessments
  8. Coordination between legal, engineering, and product
  9. Maintaining audit readiness between cycles
  10. Updating documentation based on feedback
  11. Handling scope changes during assessment
  12. Lessons learned from early AI Act certifications
Module 11. Cross-Team Collaboration for Compliance
Break down silos between data, ML, legal, and compliance teams.
12 chapters in this module
  1. Establishing shared definitions of compliance success
  2. Regular syncs between platform and AI teams
  3. Legal requirements translated into engineering controls
  4. Compliance as a shared key result in OKRs
  5. Training for engineers on AI Act fundamentals
  6. Compliance champions in each technical team
  7. Conflict resolution when security and speed clash
  8. Documentation templates that work for all roles
  9. Feedback loops from audit findings to design
  10. Onboarding new team members to compliance workflows
  11. Metrics that show compliance health across teams
  12. Celebrating successful audit outcomes together
Module 12. Future-Proofing Data Infrastructure
Design systems that adapt to evolving AI regulations beyond the AI Act.
12 chapters in this module
  1. Monitoring regulatory developments in AI governance
  2. Building modular compliance components
  3. Designing for GDPR and AI Act interoperability
  4. Preparing for potential AI liability rules
  5. Adapting to changing high-risk use case lists
  6. Extensible metadata frameworks for new requirements
  7. Benchmarking against emerging standards like ISO 42001
  8. Engaging with standards bodies and consortia
  9. Compliance architecture review cadence
  10. Training programs for ongoing team education
  11. Scaling compliance practices across new regions
  12. Maintaining agility while meeting stricter controls

How this maps to your situation

  • AI Act alignment for data infrastructure
  • Audit-ready data lineage and traceability
  • Regulatory-grade schema and access governance
  • Automated, repeatable compliance evidence

Before vs. after

Before
Spending cycles rebuilding AI compliance packages due to inconsistent data traceability and last-minute schema changes.
After
Producing regulator-ready documentation with confidence, rooted in platform design choices that support defensible, auditable systems.

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 6-8 hours of focused reading and implementation planning, paced across 4 weeks with actionable checkpoints.

If nothing changes
Continuing to treat AI compliance as a downstream documentation exercise risks repeated rework, delayed product launches, and findings during regulatory review that could impact market trust and team credibility.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this course provides technical depth tailored to data and platform architects, with direct application to systems using Parquet, Delta Lake, and cloud-scale storage , ensuring you build compliance in, not bolt it on.

Frequently asked

Is this course technical enough for senior platform engineers?
Yes. The course is designed for technical leaders and includes deep dives into storage formats, schema management, lineage, and automation pipelines.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover the EU AI Act only?
The course uses the AI Act as the anchor, but principles apply to evolving global AI regulations including upcoming US and UK frameworks.
$199 one-time. Approximately 6-8 hours of focused reading and implementation planning, paced across 4 weeks with actionable checkpoints..

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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