A focused course, tailored for you
DataOps for the Brazilian AI Act
A DataOps and ML governance offering aligned to the Brazilian AI Act high-risk articles and LGPD ANPD enforcement. Portuguese-language documentation a Brazilian customer counsel accepts.
Brazilian DataOps and ML consultants face a new regulatory shape. The Brazilian AI Act creates a high-risk classification. LGPD ANPD enforcement intensifies on training-data lawful basis. The course teaches the offering that opens the door at a regulated Brazilian customer.
$199 one-time
Tailored to your situation. Access within 24 hours. 30-day money-back.
Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.
Why this course
Brazilian DataOps and ML consultants face a new regulatory shape. The Brazilian AI Act (PL 2.338, advancing through the Senate, expected enactment in 2026) creates a high-risk classification that lands on real customer use cases: credit, hiring, public service eligibility, biometric identification. LGPD ANPD enforcement intensifies on training-data lawful basis under article 7 and special-category-data under article 11. The default DataOps playbook (which addresses orchestration, lineage, observability, quality) needs the regulatory overlay before it reaches a customer ready to be sold.
The course works through that overlay. A DataOps reference flow that meets ANPD training-data evidence requirements. An ML governance posture mapped to the Brazilian AI Act high-risk articles (impact assessment, technical documentation, record-keeping, transparency, human oversight, accuracy and robustness, conformity assessment). A Portuguese-language documentation set customer counsel will accept. A discovery script that opens the door at a regulated Brazilian customer.
The course also teaches the engagement structure: how to package the offering for a Brazilian bank, insurer, or major retailer without compiling weeks of reference material per pitch. Twelve modules with deliverables. Plus a hand-built playbook for your specific customer-sector mix.
The 12 modules
Module 1. The Brazilian DataOps and ML regulatory landscape 2026
Walkthrough of the Brazilian regulatory landscape. Brazilian AI Act PL 2.338 status. LGPD ANPD enforcement priorities. BACEN Resolução 4658 cybersecurity for FS data customers. CVM Resolução 35 for capital markets data customers. ANATEL data requirements for telco customers. Each lands on the DataOps and ML governance posture differently.
Module 2. DataOps reference flow
Build the DataOps reference flow. Source-system integration. Ingestion. Transformation. Quality. Lineage. Observability. Orchestration. The Portuguese-language metadata layer. The audit trail. The evidence chain that ANPD enforcement actions test. Includes the integration with Airflow, Dagster, dbt, Great Expectations, Monte Carlo, the Brazilian-language metadata layer, and the integration with the customer's existing data platform. Plus the audit-trail pattern that satisfies ANPD enforcement evidence requirements and the Portuguese-language documentation pattern.
Module 3. ANPD training-data evidence
Build the ANPD training-data evidence framework. The lawful-basis documentation under LGPD article 7. The special-category-data documentation under article 11. The data-subject notice. The DPIA where required. The data minimisation evidence. The retention evidence. The deletion evidence at consent withdrawal. Includes the worked example for the consent-management workflow, the legitimate-interest-assessment workflow, the special-category-data exception workflow under LGPD article 11, and the DPIA workflow under ANPD's expected pattern. Plus the integration with the customer's existing privacy-management platform.
Module 4. Brazilian AI Act high-risk classification
Map customer use cases against the Brazilian AI Act high-risk classification. Credit scoring. Hiring and employment. Public service eligibility. Biometric identification. Critical infrastructure. Each with the specific high-risk article that applies and the obligations that follow. Includes the worked example for each high-risk use case: credit-scoring obligations, hiring-and-employment obligations, public-service eligibility obligations, biometric-identification obligations, and critical-infrastructure obligations. Plus the integration with sector regulators where each use case lands.
Module 5. Impact assessment
Build the AI impact assessment framework aligned to the Brazilian AI Act. Risk identification. Risk classification. Risk-treatment plan. Stakeholder consultation. Documentation. Review cadence. Plus the Portuguese-language template customer counsel accepts. Includes the integration with the customer's existing risk-management framework, the integration with the customer's privacy-management platform, and the customer-counsel-review workflow. Plus the Portuguese-language template aligned to ANPD expected impact-assessment pattern and the customer-side-stakeholder consultation framework.
Module 6. Technical documentation
Build the technical documentation set. Model card structure. Dataset documentation structure. Lineage documentation. Performance metric documentation. Bias monitoring documentation. The Portuguese-language version that customer counsel accepts and the audit-trail version that ANPD enforcement tests. Includes the integration with the customer's existing model-registry, the integration with the customer's experiment-tracking platform, the integration with the customer's data-catalog, and the audit-trail integration. Plus the Portuguese-language model-card template aligned to ANPD expected technical-documentation pattern.
Module 7. Human oversight and transparency
Build the human oversight framework. The decision-point design. The override pattern. The audit log of overrides. The transparency notice for affected individuals. The customer-facing FAQ. The integration with the customer's contact centre and ombuds function. Includes the integration with the customer's existing contact-centre platform, the integration with the customer's ombuds function, and the customer-facing FAQ pattern. Plus the worked example for the override pattern across credit-decision, hiring-decision, public-service-eligibility-decision use cases.
Module 8. ML governance posture
Build the ML governance posture. Model risk taxonomy. Model lifecycle management. Pre-deployment review. Post-deployment monitoring. Drift detection. Bias monitoring. Performance monitoring. Decommissioning. Aligned to the Brazilian AI Act conformity assessment requirement. Includes the integration with the customer's existing model-deployment platform, the integration with the customer's monitoring stack, the integration with the customer's incident-management workflow, and the conformity-assessment evidence chain. Plus the worked example for high-risk model lifecycle management across regulated Brazilian customer use cases.
Module 9. Sector applications
Sector applications. Banking and FS under BACEN. Insurance under SUSEP. Capital markets under CVM. Telco under ANATEL. Public sector under TCU and CGU. Healthcare under ANS. Each sector adds its own overlay on the Brazilian AI Act and LGPD baseline.
Module 10. Portuguese-language documentation set
Build the Portuguese-language documentation set. Customer counsel reads in Portuguese. Customer regulator reads in Portuguese. Customer board reads in Portuguese. The translation principles, the legal-translation alignment, the term-glossary for English-derived ML terminology, and the version control. Includes the worked example for each sector overlay: banking under BACEN, insurance under SUSEP, capital markets under CVM, telco under ANATEL, public-sector under TCU and CGU, healthcare under ANS. Plus the Portuguese-language documentation pattern customer-counsel from each sector accepts.
Module 11. Discovery script
Build the discovery script for a regulated Brazilian customer. The opening questions that surface the regulatory pressure. The qualification questions that surface budget and timing. The disqualification questions. The route from a CTO conversation to a CDO conversation to a programme.
Module 12. Your 10-week build plan
Week by week. Weeks 1-2: landscape and DataOps reference flow. Weeks 3-4: ANPD evidence and Brazilian AI Act high-risk classification. Weeks 5-6: impact assessment and technical documentation. Weeks 7-8: human oversight and ML governance posture. Weeks 9-10: sector applications, Portuguese documentation set, discovery script. Deliverable: a DataOps and ML governance offering ready for the next regulated Brazilian customer.
How this addresses your situation
Specific modules that map to what you said you are dealing with.
Customer asks about lawful basis → Module 3.
Customer use case is high-risk → Module 4.
Customer needs impact assessment → Module 5.
Customer needs model card and dataset documentation → Module 6.
Customer needs human oversight → Module 7.
Customer needs Portuguese documentation → Module 10.
You need to win the pitch → Module 11.
Who it is for
For Brazilian DataOps and ML consultants, principals at boutique Brazilian data practices, senior consultants at mid-tier Brazilian data and AI firms, and senior data engineers pivoting to independent Brazilian practice.
Who this is NOT for. Pure non-Brazilian markets. Practitioners at firms with no Brazilian enterprise customer business. Pure non-data roles.
FAQ
Will this work for non-high-risk use cases?
Yes. The framework adapts. The high-risk overlay activates where applicable.
What if my customer is in a public-sector body?
Module 9 covers TCU and CGU overlays for public-sector data and AI.
Does this cover ANPD enforcement specifically?
Module 3 covers ANPD evidence requirements in depth.
What about cross-border (Brazil to EU) data flows?
Module 3 covers cross-border patterns under LGPD article 33.
What is in the implementation playbook for me specifically?
Reference flow tuned to your customer-sector mix, Portuguese documentation set with sector-specific overlays, discovery script matched to your entry point.
