Certified Deep Learning Safety Engineer (CDLSE) Certification Program by Tonex
The CDLSE program equips professionals to design, verify, and certify deep learning systems used in aerospace, automotive, and healthcare. It aligns modern ML practice with safety standards, including ISO 26262, DO-178C, and FDA AI/ML SaMD expectations. Participants learn to turn mission and regulatory needs into verifiable requirements, craft rigorous V&V plans, and build defensible assurance cases.
The curriculum covers hazard analysis tailored to data-driven models, runtime assurance for real-time operation, and evidence generation for audits and certification. It also addresses cybersecurity impacts on safety, such as data poisoning, model tampering, adversarial inputs, and supply-chain risk.
You will learn methods to harden models and deployments, preserve integrity and traceability, and coordinate safety with security operations. Graduates can argue safety with objective evidence, qualify tools, manage updates responsibly, and navigate certification pathways. The result is dependable, compliant, and secure deep learning in critical environments.
Learning Objectives:
- Translate safety and regulatory requirements into ML system specifications.
- Plan and execute neural network V&V with measurable coverage.
- Build assurance cases and objective evidence for certification.
- Engineer runtime monitoring and fail-safe mechanisms for real time.
- Manage data risk, bias, and distribution shift with traceability.
- Integrate cybersecurity controls to protect safety outcomes.
Audience:
- Safety Engineers and Architects
- Machine Learning Engineers and Data Scientists
- Systems and Software Engineers
- Quality and Regulatory Professionals
- Product and Program Managers in critical domains
- Cybersecurity Professionals
Course Modules
Module 1: Safety Foundations & Standards for DL
- Functional safety concepts and safety case thinking
- Mapping ISO 26262 to ML components
- DO-178C/DO-330 implications for learning systems
- FDA AI/ML SaMD expectations and pre-sub strategies
- SOTIF considerations for perception and uncertainty
- Safety planning, roles, and independence levels
Module 2: Requirements, Hazards, and Data Risk
- Deriving ML safety requirements and constraints
- STPA/FMEA adapted for data-driven behavior
- Dataset specifications and acceptance criteria
- Label quality, bias control, and governance
- Shift detection and operational design domain limits
- End-to-end traceability from data to decisions
Module 3: Verification & Validation of Neural Networks
- Test oracles, metamorphic and scenario testing
- Coverage metrics (neuron/path) and MC/DC analogues
- Robustness, OOD, and perturbation testing
- Explainability limits and use in safety evidence
- Formal methods (bounds, reachability, SMT cues)
- Tool qualification and reproducibility controls
Module 4: Runtime Assurance for Real-Time Systems
- Determinism, WCET, and timing budgets
- Supervisors, safety envelopes, and guards
- Redundancy, diversity, and graceful degradation
- Health monitoring, drift alarms, and rollback
- Model compression/quantization with safety checks
- Edge deployment constraints and validation gates
Module 5: Certification Evidence & Audits
- Compliance pathways across domains and authorities
- Plans, standards mapping, and process assurance
- Evidence artifacts and traceability matrices
- Problem reports, change control, and impact analysis
- Supplier management and model provenance
- Audit readiness and continuous compliance
Module 6: Cybersecurity for Safety-Critical DL
- Threats: poisoning, evasion, theft, and backdoors
- Secure ML lifecycle and model SBOM practices
- Data protection, privacy, and integrity controls
- Hardening inference pipelines and interfaces
- Adversarial resilience in real-time operation
- Cyber-safety coordination with SOC/PSIRT
Exam Domains
- Safety Governance and Policy for ML
- Dataset Integrity and Risk Management
- Assurance Evidence and Compliance Engineering
- Real-Time Deployment and Runtime Control
- Certification Strategy and Regulatory Pathways
- Adversarial Threats and Secure ML Operations
Course Delivery
The course is delivered through lectures, interactive discussions, case studies, and structured assignments led by Tonex experts. Participants receive curated online resources, readings, and templates to support practical take-home work.
Assessment and Certification
Participants are assessed via quizzes, graded assignments, and a capstone project. Upon successful completion, participants receive the Certified Deep Learning Safety Engineer (CDLSE) certificate from Tonex.
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria
To pass the Certified Deep Learning Safety Engineer (CDLSE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to lead safe, compliant, and secure AI? Enroll today. Bring your team to standardize methods, accelerate certification, and reduce risk.