“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
AgentOps and GenAIOps with Microsoft Foundry
Operate and improve a versioned Foundry AI application with release and incident controls. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSPractitioner / specialist
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration48 hours
FormatPrivate cohort
LevelPractitioner / specialist
Course CodeO02
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers AgentOps and GenAIOps with Microsoft Foundry as an MCT-led VNode ITeS program for AI platform engineers, DevOps engineers. Typical duration is 48 hours (Private cohort). Delivery stays scoped to how your teams will actually use the platform after the room empties. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
AI platform engineers, DevOps engineers
- Tailored Team Delivery
Overview
Executive overview
Operate and improve a versioned Foundry AI application with release and incident controls.
Environment and licensing
Isolated Azure subscription/resource group Scoped deployment rights and resource-provider access Approved region, quota and optional model deployment Cost budget and cleanup plan Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Working Foundry application
- Software delivery and DevOps competence
- Applied AI Engineering with Microsoft Foundry, or equivalent demonstrable experience
- Software Delivery and DevOps Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Create a reproducible Foundry release and deployment pipeline
- Prove regression gating and bounded dependency-failure recovery
- Protect workload access and sensitive operational telemetry
- Demonstrate incident diagnosis, rollback and accountable operations
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
Proposed instructor-led duration; delivery subject to environment and lab validation.
- Operational architecture and service objectives
- Versioned configuration and release
- Evaluation-driven deployment
- Tracing and monitoring
- Reliability and economics
- Operations capstone
1Module 1 · 8 hours
Operational architecture and service objectives
+
Module 1 · 8 hours
Operational architecture and service objectives
Define an operational contract and identify failure domains.
Ownership
- Assign service, model, data and tool owners
- Map operational dependencies and failure domains
Dependencies
- Define task success and availability objectives
- State measurement windows and limits
Task reliability
- Set latency and throughput expectations
- Identify data-access and safety-critical failures
Latency and cost targets
- Estimate usage and cost targets
- Define escalation and error-budget decisions
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Operational architecture and service objectives
Define an operational contract and identify failure domains.
Expected output. AI service objectives and dependency map
Acceptance. The operational contract is measurable and assigns ownership across dependencies.
2Module 2 · 8 hours
Versioned configuration and release
+
Module 2 · 8 hours
Versioned configuration and release
Package a release with pinned dependencies and reproducible configuration.
Code and prompts
- Version application code and locked dependencies
- Track prompt and tool-schema changes
Datasets
- Version evaluation datasets and reference results
- Record model deployment and configuration
Model deployment configuration
- Separate environment values from source
- Track secret references without storing secret values
Environment promotion
- Package reproducible artifacts and release notes
- Record compatibility and rollback assumptions
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Versioned configuration and release
Package a release with pinned dependencies and reproducible configuration.
Expected output. Versioned release bundle
Acceptance. A candidate release can be reconstructed from its recorded artifacts.
3Module 3 · 8 hours
Evaluation-driven deployment
+
Module 3 · 8 hours
Evaluation-driven deployment
Create a pipeline that blocks a failing release and promotes an approved version.
Regression gates
- Run regression tests before promotion
- Include quality, access and unsafe-action cases
Approval
- Define release approval and audit evidence
- Separate approval from pipeline execution privileges
Rollback
- Plan rollback for incompatible configuration changes
- Use smoke tests after deployment
Model-change evaluation
- Assess model changes on the same reference set
- Avoid automatic promotion solely on model availability
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Evaluation-driven deployment
Create a pipeline that blocks a failing release and promotes an approved version.
Expected output. Controlled deployment pipeline
Acceptance. The pipeline blocks a known regression and records approved promotion evidence.
4Module 4 · 8 hours
Tracing and monitoring
+
Module 4 · 8 hours
Tracing and monitoring
Instrument the application and isolate a failing tool without logging secrets.
OpenTelemetry concepts
- Represent requests and operations as traces/spans
- Preserve correlation across tool calls
Application Insights
- Inspect retrieval, model and API latency
- Identify timeouts and failed dependencies
Tool failures
- Minimise sensitive payloads in telemetry
- Define diagnostic access and retention
Sensitive telemetry
- Connect tracing to operational triage
- Separate observed behaviour from hidden model reasoning
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Tracing and monitoring
Instrument the application and isolate a failing tool without logging secrets.
Expected output. Observability and diagnostic record
Acceptance. A failed request is diagnosed using permitted telemetry without exposing secrets.
5Module 5 · 8 hours
Reliability and economics
+
Module 5 · 8 hours
Reliability and economics
Inject throttling and failure conditions and measure bounded recovery behaviour.
Retries
- Set bounded retry and timeout policies
- Prevent repeated state-changing actions
Timeouts
- Handle throttling and dependency unavailability
- Choose safe degradation or escalation
Rate limits
- Measure usage against workload assumptions
- Identify expensive repeated context and tool patterns
Usage and cost budgets
- Exercise recovery and concurrency boundaries
- Record what the training test does not establish about scale
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Reliability and economics
Inject throttling and failure conditions and measure bounded recovery behaviour.
Expected output. Reliability and cost test record
Acceptance. The application recovers within defined bounds and reports usage and reliability limitations.
6Module 6 · 8 hours
Operations capstone
+
Module 6 · 8 hours
Operations capstone
Handle a simulated production incident and restore a known working release.
Incident response
- Run a simulated incident with an agreed impact
- Assign incident and communication owners
Recovery
- Use evidence to locate and contain the fault
- Restore a known working release
Runbooks
- Verify recovery with functional and access checks
- Record customer-facing limitations
Service handover
- Update the runbook and follow-up actions
- Explain release and operating readiness decisions
Concept and design 2h · applied exercise 4h · review 2h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Operations capstone
Handle a simulated production incident and restore a known working release.
Expected output. Assessed Foundry operations dossier
Acceptance. The operations dossier proves controlled deployment, diagnosis and recovery.
Capstone
Handle a simulated production incident and restore a known working release. Assessed Foundry operations dossier Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Create a reproducible Foundry release and deployment pipeline 30% Prove regression gating and bounded dependency-failure recovery 25% Protect workload access and sensitive operational telemetry 25% Demonstrate incident diagnosis, rollback and accountable operations 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Foundry operations dossier
- Evidence of relevant access, quality and risk controls
- Demonstration and limitations record
Scope boundaries
Step-by-step lab instructions; Runnable solution code; Packaged source datasets; Executed environment validation
Delivery options
Count teaching hours, not calendar days. Exclude breaks. Split into 4-hour or 8-hour teaching sessions; schedule complex project work in contiguous blocks.
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Private cohort delivery
- Practitioner / specialist practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Operate and improve a versioned Foundry AI application with release and incident controls.
- Private cohort scheduling
- Equivalent-experience entry review
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.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
