“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Building Multi-Model AI Pipelines on Azure
This advanced program addresses a production challenge: many AI applications can route simpler requests to smaller models while reserving frontier models for higher-complexity work. Participants design and build multi-model routing pipelines using Azure AI Foundry, Semantic Kernel, and Prompt Flow.
VNode ITeSAdvanced
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration3 days
FormatVirtual or On-site
LevelAdvanced
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Building Multi-Model AI Pipelines on Azure as an MCT-led VNode ITeS program for AI engineers and architects designing complex, production AI systems with multiple models. Typical duration is 3 days (Virtual or On-site). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
AI engineers and architects designing complex, production AI systems with multiple models
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Design and deploy production-grade AI pipelines that orchestrate multiple LLMs using Azure AI Foundry, Semantic Kernel, and Prompt Flow to route tasks to the best-fit model automatically.
Prerequisites
- Completion of Azure OpenAI Fundamentals and Advanced LLM Engineering (or equivalent)
- Python proficiency and REST API experience
- Familiarity with Azure AI Foundry or Azure OpenAI Service
Program Outcomes
Capabilities your teams will gain
- Design and implement multi-model routing logic using Prompt Flow
- Build hybrid pipelines combining frontier and small language models (SLMs)
- Implement cost tracking, latency monitoring, and quality guardrails in production
- Apply fallback and circuit-breaker patterns for resilient AI inference
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
- Azure AI Foundry and model catalog navigation
- Semantic Kernel orchestration patterns
- Prompt Flow for multi-model routing
- Hybrid inference patterns and cost optimization
- Monitoring and guardrails in production
1Module 1
Azure AI Foundry and Model Catalog
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Module 1
Azure AI Foundry and Model Catalog
Use Azure AI Foundry to explore the model catalog, compare capabilities, and select models for a multi-model routing strategy.
- Use Azure AI Foundry to explore the model catalog
- compare capabilities
- and select models for a multi-model routing strategy
2Module 2
Semantic Kernel Orchestration
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Module 2
Semantic Kernel Orchestration
Build agent and workflow orchestrations using Semantic Kernel, including plugin registration and memory-augmented AI workflows.
- workflow orchestrations using Semantic Kernel
- including plugin registration
- memory-augmented AI workflows
3Module 3
Prompt Flow for Multi-Model Routing
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Module 3
Prompt Flow for Multi-Model Routing
Design Prompt Flow pipelines that route inference requests to the appropriate model based on task complexity scoring.
- Prompt Flow for multi-model routing
4Module 4
Hybrid Inference and Cost Optimization
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Module 4
Hybrid Inference and Cost Optimization
Implement GPT-4o and Phi-3 hybrid patterns to reduce cost while maintaining quality at scale for enterprise AI workloads.
- Implement GPT-4o
- Phi-3 hybrid patterns to reduce cost while maintaining quality at scale for enterprise AI workloads
5Module 5
Monitoring and Guardrails
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Module 5
Monitoring and Guardrails
Add production monitoring, output validation guardrails, and fallback patterns to ensure reliability and compliance in live AI systems.
- Add production monitoring
- output validation guardrails
- and fallback patterns to ensure reliability
- compliance in live AI systems
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Advanced practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Equips advanced AI teams to build cost-efficient, resilient AI pipelines that route work intelligently across multiple models while maintaining quality at scale.
- Extend to team's specific model catalog and routing criteria
- Add RAG integration into the multi-model pipeline
- Include compliance and content filtering configuration for regulated environments
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
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
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
