“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 Databricks
Operate a governed Databricks AI application with evaluation, deployment and incident evidence. 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 CodeO03
- 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 Databricks as an MCT-led VNode ITeS program for AI platform engineers, MLOps 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, MLOps engineers
- Tailored Team Delivery
Overview
Executive overview
Operate a governed Databricks AI application with evaluation, deployment and incident evidence.
Environment and licensing
Azure Databricks workspace with Unity Catalog Appropriate compute/SQL and scoped catalog/storage access AI courses: approved model access, retrieval service and MLflow Deployment courses: supported serverless/Apps/runtime access and service identity Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Working Databricks GenAI application
- DevOps competence
- Applied Generative AI Engineering with Databricks, or equivalent demonstrable experience
- Software Delivery and DevOps Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Create a governed Databricks AI release and runtime deployment
- Prove evaluation gating and runtime failure diagnosis
- Verify deployed identities and protected diagnostic access
- Demonstrate recovery and an evidence-backed operational handover
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.
- AI service operating model
- Versioning and reproducibility
- MLflow evaluation and release gates
- Tracing and production diagnosis
- Reliability, scale and cost
- Operations capstone
1Module 1 · 8 hours
AI service operating model
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Module 1 · 8 hours
AI service operating model
Map runtime, data, model and tool dependencies for the application.
Agent runtime architecture
- Map framework, server and runtime responsibilities
- Identify data, model and tool dependencies
Dependencies
- Separate development, deployment and runtime identities
- Document supported environment assumptions
Ownership
- Define business task and service objectives
- Specify critical access and quality failures
Service objectives
- Assign ownership across data and platform teams
- Plan dependency and operational reviews
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: AI service operating model
Map runtime, data, model and tool dependencies for the application.
Expected output. Operational architecture and service objectives
Acceptance. The operating model identifies measurable objectives and accountable dependency owners.
2Module 2 · 8 hours
Versioning and reproducibility
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Module 2 · 8 hours
Versioning and reproducibility
Create a reproducible release record for baseline and changed versions.
Code
- Version code, agent configuration and dependencies
- Record runtime and model configuration
Prompts
- Version prompts and tool contracts
- Identify breaking changes to consumer interfaces
Evaluation datasets
- Track evaluation data and scorers
- Preserve baseline versus candidate comparisons
Dependencies and configuration
- Package reproducible deployment artifacts
- Document configuration and rollback dependencies
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: Versioning and reproducibility
Create a reproducible release record for baseline and changed versions.
Expected output. Versioned AI release bundle
Acceptance. The release record reconstructs both the baseline and changed application.
3Module 3 · 8 hours
MLflow evaluation and release gates
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Module 3 · 8 hours
MLflow evaluation and release gates
Build a release decision workflow that rejects a quality regression.
Scorers
- Choose appropriate MLflow evaluation evidence
- Version datasets and scorer configuration
Human feedback
- Use human feedback to resolve disputed outcomes
- Avoid promoting on uncalibrated aggregate scores
Thresholds
- Define gates for quality, access and actions
- Record release approvers and exceptions
Promotion and rollback
- Promote a candidate through the selected supported route
- Retain rollback and verification evidence
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: MLflow evaluation and release gates
Build a release decision workflow that rejects a quality regression.
Expected output. Evaluation-driven release evidence
Acceptance. The release process rejects a known quality or permission regression.
4Module 4 · 8 hours
Tracing and production diagnosis
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Module 4 · 8 hours
Tracing and production diagnosis
Diagnose a supplied runtime or data-access failure using trace evidence.
Trace spans
- Inspect trace structure for the application
- Correlate retrieval, model and tool failures
Tool and retrieval failures
- Diagnose missing and denied data access
- Distinguish runtime identity from developer access
Permissions
- Inspect serving or application logs appropriately
- Record missing telemetry and instrumentation gaps
Sensitive telemetry
- Protect prompt and personal data in diagnostic output
- Define permissions and retention for investigations
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 production diagnosis
Diagnose a supplied runtime or data-access failure using trace evidence.
Expected output. Diagnostic investigation record
Acceptance. The investigation links the failed task to a supported root cause with reproducible evidence.
5Module 5 · 8 hours
Reliability, scale and cost
+
Module 5 · 8 hours
Reliability, scale and cost
Exercise failure recovery and compare operational cost assumptions.
Serving configuration
- Review serving configuration and concurrency assumptions
- Identify platform and regional constraints
Rate limits
- Apply bounded rate-limit and retry behaviour
- Choose failure and escalation policies
Failure recovery
- Measure runtime and model usage
- Separate estimated cost from actual billed consumption
Usage controls
- Exercise interrupted and failed runs within scope
- Document untested production-scale conditions
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, scale and cost
Exercise failure recovery and compare operational cost assumptions.
Expected output. Reliability and cost test report
Acceptance. Reliability tests show bounded recovery and an honest cost/capacity analysis.
6Module 6 · 8 hours
Operations capstone
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Module 6 · 8 hours
Operations capstone
Demonstrate governed release, incident diagnosis and recovery.
Deployment
- Deploy a version under the intended service identity
- Verify data and tool permissions after deployment
Access checks
- Run task and evaluation smoke tests
- Confirm trace visibility for operators
Incident handling
- Investigate a simulated production failure
- Restore a known working candidate
Runbook
- Present runbook, dependencies and ownership
- Define operating review and improvement priorities
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
Demonstrate governed release, incident diagnosis and recovery.
Expected output. Assessed Databricks operations dossier
Acceptance. The capstone includes release, quality, access and recovery evidence.
Capstone
Demonstrate governed release, incident diagnosis and recovery. Assessed Databricks operations dossier Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Create a governed Databricks AI release and runtime deployment 30% Prove evaluation gating and runtime failure diagnosis 25% Verify deployed identities and protected diagnostic access 25% Demonstrate recovery and an evidence-backed operational handover 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Databricks 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 a governed Databricks AI application with evaluation, deployment and incident evidence.
- 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
