A tailored course, built for your situation
Production-Grade MLOps Foundations for Regulated Industries
Implement compliant, auditable, and scalable machine learning systems with confidence
$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.
The gap between experimental models and production systems that pass audits
The situation this course is for
Teams build powerful models, but struggle when it comes to versioning, reproducibility, and change tracking under regulatory review. The handoffs between data science, engineering, and compliance often break down, leading to delays, rework, or failed certifications.
Who this is for
Technical leaders and compliance professionals in financial services, healthcare, energy, or government-adjacent tech roles who need to ship models that stand up to audit cycles and operational scrutiny
Who this is not for
This is not for data scientists focused solely on modeling or academic research. It’s also not for those seeking introductory AI/ML literacy courses or non-regulated industry use cases.
What you walk away with
- Architect model deployment pipelines that support full traceability and compliance
- Implement version-controlled workflows for models and data that satisfy audit requirements
- Integrate risk and governance checks directly into CI/CD for machine learning
- Document model decisions in ways that meet legal and regulatory expectations
- Lead cross-functional teams through compliant model lifecycle management
The 12 modules (with all 144 chapters)
Module 1. Introduction to Regulated ML Systems
Define the scope, constraints, and expectations of machine learning in high-compliance environments.
12 chapters in this module
- Defining regulated AI use cases
- Regulatory triggers and thresholds
- Compliance by design principles
- Stakeholder mapping in ML projects
- Risk classification frameworks
- Model inventory requirements
- Documentation standards overview
- Audit readiness fundamentals
- Cross-functional team roles
- Governance boundaries
- Lifecycle oversight models
- Case study: Model approval in a tier-1 bank
Module 2. Data Governance for Model Inputs
Establish traceable, versioned, and auditable data pipelines feeding ML models.
12 chapters in this module
- Data provenance tracking
- Schema versioning strategies
- Data quality checkpoints
- Bias detection in input sets
- Data retention policies
- Anonymization for training
- Data lineage tooling
- Audit log integration
- Cross-system consistency
- Regulatory data handling
- Change approval workflows
- Case study: Healthcare data pipeline compliance
Module 3. Model Development Standards
Embed compliance into the model creation phase without slowing innovation.
12 chapters in this module
- Pre-registration of model intent
- Version control for model code
- Reproducibility environments
- Model cards and metadata
- Ethical impact screening
- Baseline performance thresholds
- Documentation templates
- Peer review protocols
- Model registry integration
- Code audit readiness
- Security scanning in dev
- Case study: Insurance underwriting model
Module 4. Version Control and Reproducibility
Ensure every model output can be traced back to its exact code and data state.
12 chapters in this module
- Git strategies for ML
- Model checksums and hashes
- Environment pinning
- Containerized execution
- Rebuild automation
- Model signature standards
- Checkpoint validation
- Rollback procedures
- Cross-platform consistency
- Audit trail generation
- Version naming conventions
- Case study: Regulatory rollback request
Module 5. CI/CD for Regulated Models
Automate deployment pipelines while ensuring compliance gates are enforced.
12 chapters in this module
- Pipeline segmentation
- Automated compliance checks
- Approval gate design
- Staging environments
- Canary release controls
- Rollback automation
- Monitoring integration
- Secrets management
- Access control policies
- Audit logging in pipelines
- Change tracking
- Case study: Phased rollout in capital markets
Module 6. Model Validation and Testing
Implement robust testing frameworks that satisfy both technical and regulatory standards.
12 chapters in this module
- Statistical performance baselines
- Drift detection thresholds
- Backtesting rigor
- Edge case simulation
- Fairness testing protocols
- Residual analysis
- Model stability checks
- Sensitivity analysis
- Third-party validation
- Test documentation
- Automated test suites
- Case study: Loan approval model validation
Module 7. Model Monitoring in Production
Track model behavior post-deployment to detect degradation, drift, or compliance deviations.
12 chapters in this module
- Performance KPIs
- Data drift alerts
- Concept drift detection
- Latency monitoring
- Input distribution tracking
- Feedback loop integration
- Alert prioritization
- Model decay signals
- Automated reporting
- Human-in-the-loop triggers
- Escalation workflows
- Case study: Real-time fraud model oversight
Module 8. Audit and Documentation Practices
Prepare comprehensive, defensible records for internal and external reviews.
12 chapters in this module
- Model decision logs
- Change justification records
- Version comparison reports
- Compliance assertion templates
- Third-party audit prep
- Internal review cycles
- Document retention policies
- Regulatory correspondence
- Model decommission logs
- Evidence packaging
- Audit trail navigation
- Case study: Regulatory inspection response
Module 9. Governance and Oversight Frameworks
Design organizational structures that ensure accountability and transparency.
12 chapters in this module
- Model governance boards
- Risk tier classification
- Change approval hierarchies
- Escalation protocols
- Model inventory systems
- Oversight reporting
- Ethics review panels
- Model sunsetting policies
- Cross-department coordination
- Regulatory liaison roles
- Policy update cycles
- Case study: Central bank reporting
Module 10. Security and Access Controls
Protect model assets and data with enterprise-grade safeguards.
12 chapters in this module
- Role-based access design
- Model encryption
- API security
- Network segmentation
- Privilege escalation
- Audit trail access
- Penetration testing
- Incident response
- Data leakage prevention
- Compliance with security standards
- Access logging
- Case study: Breach prevention in health tech
Module 11. Change and Incident Management
Handle model updates and outages with minimal risk and maximum transparency.
12 chapters in this module
- Change request workflows
- Impact assessment
- Rollback planning
- Incident classification
- Post-mortem documentation
- Stakeholder notification
- Service level agreements
- Uptime monitoring
- Model retraining triggers
- Version rollback testing
- Compliance impact analysis
- Case study: Model failure in payment processing
Module 12. Scaling MLOps Across the Organization
Extend compliant practices across teams, systems, and business units.
12 chapters in this module
- Center of excellence models
- Standardization across teams
- Toolchain harmonization
- Training and enablement
- Cross-team collaboration
- Knowledge sharing
- Policy alignment
- Vendor integration
- Multi-cloud MLOps
- Enterprise risk integration
- Board-level reporting
- Case study: Global bank MLOps rollout
How this maps to your situation
- Preparing for regulatory scrutiny
- Scaling models across departments
- Responding to audit findings
- Implementing model governance
Before vs. after
Before
Uncertainty around compliance expectations, fragmented tooling, and manual documentation processes that slow down deployment.
After
A clear, repeatable framework for deploying and managing models that meet regulatory standards, with templates and workflows ready for immediate use.
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 40 hours of structured learning, designed for professionals to complete at their own pace over 6, 8 weeks.
If nothing changes
Organizations risk delayed approvals, failed audits, or operational outages when models lack proper governance, traceability, and change control, especially as regulatory scrutiny intensifies.
How this compares to the alternatives
Unlike generic MLOps courses, this program focuses exclusively on implementation in regulated environments, offering field-tested templates, compliance-specific workflows, and audit-ready documentation patterns not found in generalist offerings.
Frequently asked
Who is this course designed for?
It’s for engineers, compliance leads, and technical product managers working in regulated sectors who need to deploy models that meet strict governance and audit standards.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there hands-on coding?
No, this is a text-based, implementation-focused course with templates and examples. It’s designed for practitioners who need to understand and apply systems, not write code from scratch.
$199 one-time. Approximately 40 hours of structured learning, designed for professionals to complete at their own pace over 6, 8 weeks..
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