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.
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.
FAQ Assistant