Technical Implementation Track
AI DevOps, SecOps & FinOps for Production-Grade AI
Design, build and operate industrial-grade AI systems with a complete IAOps framework covering AI DevOps, security and FinOps across the full lifecycle — from pipeline design to production operations and cost optimisation.
CPF EligibleCompte Personnel de FormationQualiopi CertifiedCertified training organisation
Certificate of completion included
Course overview
As AI systems scale, teams quickly discover that:
- classic DevOps patterns are insufficient,
- security risks multiply,
- costs grow faster than usage,
- failures are harder to detect and roll back.
This three-day technical programme delivers a deep IAOps approach so your teams can:
- build robust AI delivery pipelines,
- secure AI artefacts and inference services,
- operate AI workloads reliably at scale,
- control and optimise AI costs over time.
The course is tool-agnostic, cloud-ready and rooted in real production constraints.
Learning objectives
By the end of the programme, participants will be able to:
- Design a complete IAOps reference architecture.
- Implement CI/CD pipelines for AI systems.
- Ensure reproducibility and traceability of data, models and prompts.
- Secure AI pipelines, artefacts and runtime environments.
- Monitor AI-specific risks such as drift or misuse.
- Apply FinOps practices to AI workloads.
- Build and maintain a production-ready IAOps roadmap.
Who should attend?
- DevOps and platform engineers.
- AI / ML engineers deploying models to production.
- Cloud engineers and solution architects.
- Security engineers and SecOps teams.
- FinOps practitioners supporting AI platforms.
- Technical leads and SREs accountable for AI reliability.
Prerequisites
- Solid experience with DevOps or cloud-native environments.
- Familiarity with CI/CD pipelines.
- Basic understanding of AI or ML workloads (training vs inference).
Course content
Day 1 – IAOps Foundations & AI DevOps
Module 1 – IAOps fundamentals for production environments
Technical foundations
- Why traditional DevOps fails for AI workloads.
- From MLOps to IAOps: expanded operational scope.
- AI system components: data, models, prompts, inference services.
- Non-determinism, drift and reproducibility challenges.
REX – Common failures
- Pipelines that cannot be replayed.
- Models that cannot be explained or rolled back.
- Environments that diverge silently.
Module 2 – AI DevOps: CI/CD pipelines for AI systems
Pipeline design
- CI/CD for data ingestion, model training, packaging and deployment.
- Versioning strategies for data, models and prompts.
- Environment parity across dev, staging and prod.
Deployment patterns
- Batch vs real-time inference.
- Canary, shadow and blue/green deployments.
- Rollback strategies for AI services.
REX – What breaks in production
- Accuracy without stability.
- Undetected data drift after deployment.
Module 3 – Reproducibility, traceability and auditability
- Dataset lineage and provenance.
- Model and prompt traceability.
- Build artefact integrity.
- Operational audit trails.
Day 2 – Observability, Reliability & AI SecOps
Module 4 – Observability and reliability of AI workloads
Monitoring
- Model metrics vs system metrics.
- Data, concept and prompt drift detection.
- Latency, throughput and error rates.
Reliability engineering
- SLOs and SLIs for AI services.
- Alerting strategies and stop conditions.
- Human-in-the-loop escalation patterns.
REX – Incident patterns
- Silent model degradation.
- False positives in drift detection.
Module 5 – AI SecOps: threat model and attack surface
Threat landscape
- Data poisoning and model theft.
- Prompt injection and abuse.
- Supply-chain vulnerabilities.
Security design
- Threat modelling for AI systems.
- Security boundaries across pipelines.
Module 6 – Implementing AI SecOps controls
- IAM for AI pipelines and inference.
- Secrets and key management.
- Secure artefact storage and signing.
- Logging, auditability and forensic readiness.
REX – Security pitfalls
- Over-trusting model outputs.
- Lack of isolation between environments.
Day 3 – AI FinOps, Governance & IAOps Roadmap
Module 7 – AI FinOps: understanding and controlling AI costs
Cost drivers
- Training vs inference economics.
- GPU and accelerator utilisation.
- Storage and data movement costs.
- Third-party and foundation model pricing.
Hidden costs
- Retries, hallucinations and misuse.
- Over-provisioned environments.
Module 8 – Implementing FinOps practices for AI
Operational practices
- Cost allocation per model, team or service.
- Budget thresholds and automated alerts.
- Architecture-level optimisation levers.
- Linking cost metrics to usage and value.
REX – Cost failures
- “Successful” models that are financially unsustainable.
Module 9 – IAOps operating model & implementation roadmap
Operating model
- Roles and responsibilities across DevOps, SecOps and FinOps.
- Platform vs product team ownership.
- Governance without blocking delivery.
Roadmap
- Technical maturity assessment.
- Prioritising implementation gaps.
- Defining measurable milestones.
- Continuous improvement loop.
Deliverable: a 3–12 month IAOps technical implementation roadmap.
Key benefits
- End-to-end, production-focused IAOps expertise.
- Reduced operational, security and financial risks.
- Stronger reliability and observability of AI systems.
- Clear cost accountability and optimisation levers.
- Immediately reusable implementation patterns.
Teaching methods
- Deep technical walkthroughs.
- Architecture and pipeline design sessions.
- Analysis of real production incidents.
- Implementation checklists and reference architectures.
- Instructor-led, experience-driven discussions.
Assessment & certification
Continuous assessment through technical scenarios, validation of the IAOps implementation roadmap and a certificate of completion issued at the end of the programme.
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