Reliability Engineering for AI-Driven Systems (RE-AIS) Essentials Training by Tonex
AI-enabled platforms are only as valuable as their reliability under real-world stress. This course gives practitioners a practical blueprint for building, operating, and improving dependable AI systems that meet business and mission goals. You will learn how to engineer resilience across data pipelines, models, and services using measurable objectives, robust architectures, and disciplined operations. Cybersecurity considerations are woven through the lifecycle to protect availability, integrity, and confidentiality during failures and attacks. You will connect reliability tactics—redundancy, graceful degradation, and rapid recovery—to cyber defense-in-depth so that adversarial disruptions do not cascade into outages or safety events.
Learning Objectives
- Define reliability, availability, resilience, and maintainability for AI-enabled services
- Translate business goals into SLOs, SLIs, and error budgets for data, model, and service layers
- Design fault-tolerant, self-healing MLOps architectures with rollback and recovery paths
- Detect and mitigate data drift, concept drift, and model decay with automated monitors
- Implement incident response and post-incident learning tailored to AI failure modes
- Strengthen system posture against adversarial disruptions with reliability-by-design that includes cybersecurity
Audience
- Reliability Engineers and SREs
- Machine Learning Engineers and Data Engineers
- AI/ML Architects and Platform Owners
- DevOps and Site Operations Teams
- Product Managers and Technical Leaders
- Cybersecurity Professionals
Module 1 – Reliability Foundations
- Reliability vs availability vs resilience
- Failure modes of AI systems
- SLIs, SLOs, and error budgets
- Reliability metrics and MTTR
- Risk appetite and prioritization
- Service dependency mapping
Module 2 – Data and Model Health
- Data quality dimensions
- Drift detection strategies
- Label and ground-truth hygiene
- Feature store reliability
- Model decay and retraining triggers
- Shadow, canary, and A/B rollout
Module 3 – Resilient MLOps
- CI/CD for models and pipelines
- Immutable artifacts and versioning
- Rollback and blue-green deploys
- Orchestrators and queue backpressure
- Idempotency and retries with jitter
- Secrets and config management
Module 4 – Fault-Tolerant Architecture
- Graceful degradation patterns
- Bulkheads and circuit breakers
- Caching and timeouts tuning
- Replication and quorum choices
- Multi-AZ/region failover design
- Dependency isolation and fusing
Module 5 – Observability and Response
- Telemetry for data, model, service
- SLO dashboards and alert hygiene
- Anomaly detection for behavior
- Incident runbooks and roles
- Post-incident reviews and actions
- Chaos and game days for AI
Module 6 – Safety, Security, Compliance
- Threats to availability in AI stacks
- Secure-by-default reliability controls
- Adversarial robustness safeguards
- Safety constraints and guardrails
- Policy, audit, and traceability
- Business continuity and testing
Build AI your customers can trust. Enroll in Reliability Engineering for AI-Driven Systems Essentials by Tonex to master the patterns, metrics, and operating disciplines that keep intelligent services dependable, secure, and ready for growth.