“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Applied Generative AI Engineering with Databricks
Build an evaluated Databricks AI application using governed enterprise knowledge. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSPractitioner / specialist
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration56 hours
FormatPrivate cohort
LevelPractitioner / specialist
Course CodeE03
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Applied Generative AI Engineering with Databricks as an MCT-led VNode ITeS program for AI engineers, Data engineers. Typical duration is 56 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 engineers, Data engineers
- Tailored Team Delivery
Overview
Executive overview
Build an evaluated Databricks AI application using governed enterprise knowledge.
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
- Python API and SQL competence
- Databricks engineering foundations
- AI and Generative AI Foundations, or equivalent demonstrable experience
- Databricks Engineering Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Deliver a Databricks application using governed data and tools
- Compare quality and diagnose failures using evaluation and traces
- Prove intended catalog and deployed runtime access boundaries
- Provide deployment configuration and accountable knowledge ownership
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 application architecture and model access
- Preparing governed knowledge
- Retrieval and context engineering
- Agent and tool engineering
- MLflow evaluation and tracing
- Governed deployment and operations
1Module 1 · 8 hours
AI application architecture and model access
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Module 1 · 8 hours
AI application architecture and model access
Create an authenticated model client and baseline test set.
Use-case contract
- Define AI task, response and latency contracts
- Choose deployment and consumer boundaries
Foundation model access
- Compare approved model endpoints and access methods
- Record provider and regional restrictions
Serving choices
- Choose serving and runtime responsibilities
- Avoid coupling application logic to one endpoint unnecessarily
Cost assumptions
- Create baseline cost and quality assumptions
- Capture exact configuration for comparison
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 application architecture and model access
Create an authenticated model client and baseline test set.
Expected output. Application contract and baseline model results
Acceptance. The application contract and model client are reproducible and authenticated.
2Module 2 · 8 hours
Preparing governed knowledge
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Module 2 · 8 hours
Preparing governed knowledge
Prepare traceable source content and deny access to an unauthorised persona.
Documents and tables
- Inventory documents, tables and authoritative owners
- Define business purpose for each source
Metadata
- Preserve document identifiers and table provenance
- Track update and withdrawal requirements
Unity Catalog access
- Map Unity Catalog groups and service identities
- Test consumer and workload permissions separately
Quality and freshness
- Validate completeness, sensitivity and freshness
- Exclude unsupported or unapproved source content
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: Preparing governed knowledge
Prepare traceable source content and deny access to an unauthorised persona.
Expected output. Governed knowledge dataset
Acceptance. The prepared dataset has traceable provenance and documented access boundaries.
3Module 3 · 8 hours
Retrieval and context engineering
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Module 3 · 8 hours
Retrieval and context engineering
Build a retrieval path and compare relevant and irrelevant context results.
Embeddings
- Choose embeddings and index compatibility
- Record changes requiring index refresh
AI Search concepts
- Compare chunking and metadata strategies
- Measure evidence coverage for reference questions
Filtering
- Filter by source and access metadata
- Avoid retrieving restricted evidence before generation
Retrieval evaluation
- Compare retrieval variants on a fixed question set
- Inspect irrelevant and missing context failures
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: Retrieval and context engineering
Build a retrieval path and compare relevant and irrelevant context results.
Expected output. Retrieval implementation and evidence
Acceptance. The retrieval implementation meets agreed relevance and access checks.
4Module 4 · 8 hours
Agent and tool engineering
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Module 4 · 8 hours
Agent and tool engineering
Implement a bounded tool-enabled assistant using the validated platform route.
Framework boundaries
- Choose a framework and bounded agent loop
- Define state, stop conditions and escalation
Agent Bricks architecture
- Map agent server and runtime responsibilities
- Use the current validated Agent Bricks authoring route
Tools and MCP
- Integrate tools with explicit input/output schemas
- Review MCP trust and resource permissions
Approval and state
- Require approval for state-changing actions
- Handle duplicate and failed tool calls
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: Agent and tool engineering
Implement a bounded tool-enabled assistant using the validated platform route.
Expected output. Agent code and tool contract
Acceptance. The agent executes only allowed tools and exposes failures without fabricating completion.
5Module 5 · 8 hours
MLflow evaluation and tracing
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Module 5 · 8 hours
MLflow evaluation and tracing
Evaluate two versions and diagnose a retrieval or tool failure.
Datasets
- Create versioned evaluation examples and reference evidence
- Separate development and comparison sets
Scorers
- Use appropriate deterministic and judge scorers
- Calibrate uncertain results with human feedback
Human feedback
- Inspect model, retrieval and tool spans
- Diagnose a failed task from trace evidence
Trace inspection
- Compare baseline and revised versions
- Report quality, latency and usage trade-offs
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 tracing
Evaluate two versions and diagnose a retrieval or tool failure.
Expected output. MLflow evaluation and trace record
Acceptance. The evaluation and trace record identifies specific causes and reproducible version differences.
6Module 6 · 8 hours
Governed deployment and operations
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Module 6 · 8 hours
Governed deployment and operations
Deploy with least privilege and test allowed and denied operations.
Runtime and Apps requirements
- Check Apps/runtime/serverless prerequisites
- Separate notebook compute from deployed serving
Service identities
- Assign scoped workload identity permissions
- Test runtime access independently of developer access
Permissions
- Configure environment and dependencies reproducibly
- Record release and rollback assumptions
Release and cost controls
- Set usage visibility and ownership
- Review network and data exposure boundaries
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: Governed deployment and operations
Deploy with least privilege and test allowed and denied operations.
Expected output. Deployment and access test record
Acceptance. The deployed agent runs under its intended identity and denies unsupported data/tool access.
7Module 7 · 8 hours
Applied GenAI capstone
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Module 7 · 8 hours
Applied GenAI capstone
Demonstrate the application and defend its evaluation, deployment and data-access decisions.
Reproducibility
- Demonstrate the agreed knowledge and action scenarios
- Apply held-out acceptance cases
Acceptance thresholds
- Present quality and access results
- Explain retrieval and model limitations
Security boundaries
- Provide deployment configuration and change records
- Document source and operational owners
Handover
- Defend choices under a changed requirement
- Identify work needed beyond the bounded scenario
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: Applied GenAI capstone
Demonstrate the application and defend its evaluation, deployment and data-access decisions.
Expected output. Assessed Databricks GenAI application
Acceptance. The capstone combines working AI behaviour with governed data and operational evidence.
Capstone
Demonstrate the application and defend its evaluation, deployment and data-access decisions. Assessed Databricks GenAI application Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Deliver a Databricks application using governed data and tools 30% Compare quality and diagnose failures using evaluation and traces 25% Prove intended catalog and deployed runtime access boundaries 25% Provide deployment configuration and accountable knowledge ownership 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Databricks GenAI application
- 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: Build an evaluated Databricks AI application using governed enterprise knowledge.
- 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
