Enterprise Program Brief
LLMOps and GenAIOps Engineering
Operationalise a supplied AI application using reproducible packaging, identity and configuration management, CI checks, telemetry, bounded failure handling, and rollback. Participants work on application-level operations and identify the additional work needed for broader AI platforms.
Program Snapshot
Engagement scoped to cohort
- Coming soon
- Private cohort / virtual ILT
- Typical response: 1 business day
LLMOps and GenAIOps Engineering is a proposed VNode ITeS programme for AI engineering and delivery. The outlined duration is 32 hours, excluding breaks, and remains subject to pilot validation. Enrolment is not open yet. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
DevOps engineers, cloud engineers, SREs, AI engineers, and application owners with deployment experience.
- AI engineering and delivery
- AI Engineering and Delivery Roles
- Tailored Team Delivery
Overview
Executive overview
Operationalise a supplied AI application using reproducible packaging, identity and configuration management, CI checks, telemetry, bounded failure handling, and rollback. Participants work on application-level operations and identify the additional work needed for broader AI platforms.
Environment and licensing
One Azure hosting path selected for the cohort, GitHub Actions, Azure Monitor/Application Insights, and an authorised model endpoint. Local container and recorded telemetry fixtures provide a labelled fallback. Infrastructure permissions and paid resource ownership are prerequisites.
Prerequisites
- Git, CI/CD, containers or equivalent application packaging, cloud identity fundamentals, Python application operations, and a working understanding of LLM requests. A tested source application is supplied.
Program Outcomes
Capabilities your teams will gain
- Version application prompts, retrieval assets, configuration, and evaluation data.
- Package and release an AI application through defined gates.
- Trace failures without unnecessarily logging sensitive data.
- Measure latency, usage, and cost by workload.
- Rehearse incident response, rollback, and model-change evaluation.
Enterprise Delivery
Discuss team requirements
Coming soon. Contact VNode ITeS to discuss team requirements and programme availability. This independently developed programme is not an official vendor certification, and enrolment is not open yet.
Curriculum
Curriculum roadmap
- GenAI operating model and service objectives (3 hours)
- Packaging and environment configuration (4 hours)
- Identity and controlled access (4 hours)
- CI and release quality gates (5 hours)
- Observability and operational diagnosis (4 hours)
- Reliability and recovery (4 hours)
1Module 1 · 3 hours
GenAI operating model and service objectives
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Module 1 · 3 hours
GenAI operating model and service objectives
An AI application changes through code, prompts, data, model versions, and platform configuration. Define who owns each asset and what useful service behaviour means. Quality objectives and incident expectations should fit the workflow rather than copy a generic infrastructure target.
- Asset lifecycle and ownership
- Service indicators and task-level measures
- Dependency and failure inventory
Planned lab. Create an operating model and service objectives for a supplied support application.
Expected output. Ownership map and service objectives.
2Module 2 · 4 hours
Packaging and environment configuration
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Module 2 · 4 hours
Packaging and environment configuration
Reproducible deployment starts with known dependencies and explicit configuration. Keep secrets and environment-specific identities outside the application package. A repeatable startup and health check helps separate packaging problems from model-quality problems.
- Dependency locking and application packaging
- Environment settings and secrets
- Health checks and deployment validation
Planned lab. Package the supplied service, run it locally, and validate its configuration and health behaviour.
Expected output. Versioned application package and setup record.
3Module 3 · 4 hours
Identity and controlled access
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Module 3 · 4 hours
Identity and controlled access
The application identity needs only the permissions required for its task. Separate management permissions from runtime access and source permissions. Review how local development authentication differs from the deployed workload identity.
- Runtime identity and least privilege
- Model and source access
- Secret rotation and configuration ownership
Planned lab. Configure the selected sandbox identity path and demonstrate an allowed call and a denied operation.
Expected output. Access inventory and verified permission boundary.
4Module 4 · 5 hours
CI and release quality gates
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Module 4 · 5 hours
CI and release quality gates
A release should identify all changed AI application assets and run the corresponding checks. Reject a candidate on a blocking control failure even when it builds successfully. Keep model-generated scoring distinct from deterministic tests in the report.
- Build and behaviour checks
- Evaluation integration and approvals
- Artifact promotion and release records
Planned lab. Build a pipeline that packages a candidate, runs tests and evaluations, and records an explicit release decision.
Expected output. Pipeline and release evidence.
5Module 5 · 4 hours
Observability and operational diagnosis
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Module 5 · 4 hours
Observability and operational diagnosis
Useful telemetry connects a user task with application steps, model calls, and dependencies. Record the minimum information needed to diagnose failures. Correlation and version fields help identify whether a regression followed a prompt, source, or deployment change.
- Traces, correlation, and version metadata
- Latency and token usage
- Telemetry minimisation and retention choices
Planned lab. Diagnose a seeded failure using traces and build a small operational dashboard.
Expected output. Trace walkthrough and dashboard specification.
6Module 6 · 4 hours
Reliability and recovery
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Module 6 · 4 hours
Reliability and recovery
Bound retries and distinguish safe reads from state-changing operations. A fallback must preserve the workflow contract and should not quietly change permissions or quality expectations. Test recovery procedures with realistic failure timing.
- Timeouts, retry budgets, and idempotency
- Fallback decisions and dependency outages
- Rollback and recovery rehearsal
Planned lab. Inject model unavailability and a failed write, recover safely, and revert a candidate version.
Expected output. Failure test record and runbook.
7Module 7 · 4 hours
Cost performance and model change
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Module 7 · 4 hours
Cost performance and model change
Cost per request is only part of the operating picture. Account for retrieval, hosting, retries, and review effort where relevant. Compare model or context changes against quality and latency evidence before adopting a cheaper or faster configuration.
- Cost allocation and workload budgets
- Context and caching trade-offs
- Model upgrades and regression checks
Planned lab. Compare two permitted configurations using a fixed workload and document quality, usage, latency, and excluded costs.
Expected output. Cost-performance comparison and upgrade plan.
8Module 8 · 4 hours
Operations capstone
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Module 8 · 4 hours
Operations capstone
Release and operate a bounded application candidate using the evidence assembled in earlier modules. Demonstrate a failure and recovery, then hand the service to another operator using only the runbook.
- Candidate release and incident rehearsal
- Evidence and ownership review
- Operational handover
Planned lab. Deploy the supplied application to the selected sandbox path, run the incident drill, and present a handover.
Expected output. Release package and operating handover.
Capstone
A versioned support AI service with release gates, traces, cost assumptions, and a demonstrated rollback and recovery drill.
Assessment
Release reproducibility 25%; identity/configuration 20%; observability 20%; failure recovery 25%; handover 10%. A missing or unsafe recovery procedure requires remediation.
Participant deliverables
- Application deployment package
- CI and evaluation gates
- Telemetry and cost report
- Runbook and recovery evidence
Scope boundaries
Full GPU cluster administration, model pretraining, multi-cloud platform certification, and a guarantee of production availability. **Delivery options:** 4 learning days of 8 hours, or 8 sessions of 4 hours. Learning hours exclude breaks and include module exercises and capstone time. Setup is completed before the course.
Delivery Models
Delivery models
Engagement Fit
Engagement fit
- Private cohort delivery
- Intermediate to Advanced practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Deploy and operate versioned AI applications with quality gates and observable costs
- 4 learning days of 8 hours, or 8 sessions of 4 hours. Learning hours exclude breaks and include module exercises and capstone time. Setup is completed before the course.
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
