Can the program be customized?

Yes. Develop AI cloud solutions on Azure supports customization around lab depth, demo context, certification focus, and team-specific scenarios.

Yes. This program includes instructor-led labs and practical exercises aligned to real implementation patterns.

How does this align with official certification or curriculum?

This program is aligned to the official Microsoft Learn curriculum and learning path referenced for this track.

How are prerequisites assessed?

We review prerequisite readiness during scoping and can recommend preparatory modules or a skills checkpoint before cohort start.

What happens after the program?

Private cohorts receive a handover summary, recommended next learning paths, and optional follow-on advisory for rollout or certification readiness.

Microsoft Official Curriculum

Develop AI cloud solutions on Azure

This course teaches developers how to create, monitor, and troubleshoot AI solutions on Microsoft Azure. Students will learn how to implement Azure compute and containerization patterns to host applications, build serverless APIs with Azure Functions, and integrate services using event‑driven and message‑based architectures such as Azure Service Bus and Event Grid. The course also covers working with Azure data services that support AI workloads, including designing and querying solutions with Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for caching, streaming, and vector search. By the end of the course, developers will be able to connect services, orchestrate AI workflows, and build secure, scalable, and observable AI‑driven applications on Azure.

Role-Based Certification PrepTrack: AI-200T00Official Source: Microsoft LearnMicrosoftIntermediate

Plan Private Delivery Consult a Solutions Architect

Download Program Brief (PDF)

Program Snapshot

Engagement scoped to cohort

Duration5 days

FormatVirtual, On-site, or Hybrid

LevelIntermediate

Course CodeAI-200T00

Certification TrackAI-200T00

SubjectAI

MCT-led delivery
Private cohort / virtual ILT
Typical response: 1 business day

Scoping call typically scheduled within one business day

VNode ITeS delivers Develop AI cloud solutions on Azure as an MCT-led Microsoft program for Developer. Typical duration is 5 days (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Certification prep maps to AI-200T00 without turning the week into a dump of Learn modules. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.

Audience Profile

Built for these roles

This course is designed for developers who build backend and AI‑driven applications on Azure and need practical skills in containerized compute, data services for AI, event‑driven workflows, and application security and monitoring.

Developer
AI
Certification Readiness
Tailored Team Delivery

Overview

Executive overview

As a candidate for this Microsoft Certification, you're responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components.

Prerequisites

Relevant foundational experience in the target technology area.
Comfort with hands-on labs in a cloud or GPU-accelerated environment.

Program Outcomes

Capabilities your teams will gain

Strengthen capability in developer scenarios
Strengthen capability in azure scenarios

Enterprise Delivery

Official learning & delivery options

Review the vendor learning resource, then book a consultation or request a private cohort for your team.

View Official Learning Book a Consultation Request Private Cohort Open in learning platform Program brief (PDF)

Curriculum

Curriculum roadmap

Developer
Azure

Module 1

Implement container application hosting on Azure

Learn how to deploy, configure, and troubleshoot containerized applications on Azure.

Store and manage containers in Azure Container Registry
Deploy containers to Azure App Service
Run sidecar-enabled AI applications on Azure App Service

Module 2

Deploy and manage apps on Azure Container Apps

Learn how to deploy, manage, and scale containerized applications and securely run AI-generated code on Azure Container Apps.

Deploy containers to Azure Container Apps
Manage containers in Azure Container Apps
Scale containers in Azure Container Apps
Run AI-generated code securely in Azure Container Apps dynamic sessions

Module 3

Deploy and monitor applications on Azure Kubernetes Service

Learn how to deploy, configure, and troubleshoot apps on Azure Kubernetes Service.

Deploy applications to Azure Kubernetes Service
Configure applications on Azure Kubernetes Service
Monitor and troubleshoot applications on Azure Kubernetes Service

Module 4

Develop AI solutions with Azure Cosmos DB for NoSQL

Learn how to develop AI solutions using Azure Cosmos DB for NoSQL, including vector search and query optimization for RAG pipelines and semantic retrieval.

Build queries for Azure Cosmos DB for NoSQL
Implement vector search on Azure Cosmos DB for NoSQL
Optimize query performance for Azure Cosmos DB for NoSQL

Module 5

Develop AI solutions with Azure Database for PostgreSQL

Learn how to develop AI solutions using Azure Database for PostgreSQL, including vector search with pgvector for RAG pipelines and semantic retrieval.

Build and query with Azure Database for PostgreSQL
Implement vector search with Azure Database for PostgreSQL
Optimize vector search in Azure Database for PostgreSQL

Module 6

Enhance AI solutions with Azure Managed Redis

Learn how to use Azure Managed Redis to enhance your AI solutions, including caching strategies, data operations, event messaging, and vector storage.

Implement data operations in Azure Managed Redis
Implement event messaging with Azure Managed Redis
Implement vector storage in Azure Managed Redis

Module 7

Integrate backend services for AI solutions

Learn how to integrate backend services for AI solutions on Azure.

Queue and process AI operations with Azure Service Bus
Develop event-driven AI workflows with Azure Event Grid
Build serverless AI backends with Azure Functions
Orchestrate durable AI workflows with Azure Durable Functions

Module 8

Manage application secrets and configuration for AI solutions

Learn how to manage application secrets and configuration for AI solutions on Azure using Azure Key Vault and Azure App Configuration.

Manage application secrets with Azure Key Vault
Manage application settings with Azure App Configuration

Module 9

Observe and troubleshoot apps on Azure

Learn how to instrument distributed applications with OpenTelemetry and analyze telemetry data with Azure Monitor to observe and troubleshoot AI solutions on Azure.

Instrument an app with OpenTelemetry
Analyze app telemetry with logs and metrics

Delivery Models

Delivery models

Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend

Engagement Fit

Engagement fit

Certification readiness
Implementation-focused labs
Private cohort delivery
Intermediate practitioner depth

Enterprise Customization

Enterprise customization

Tailor this program to your organization's priorities: Builds current Microsoft credential readiness for Azure AI Cloud Developer Associate using the official Microsoft Learn skill outline.

Align labs to your Microsoft tenant and workload scenarios
Add readiness checks and exam preparation reviews
Extend delivery with role-specific implementation workshops

Credentials

Certification & official source

•AI-200T00

Aligned to the official Microsoft Learn course and learning path for this program.

View Official Microsoft Learn Page

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.

Program brief (PDF)View Official Microsoft Learn Page

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.”

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

Read engagement

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

Read engagement

Delivery Capability

Enterprise-grade instruction

View delivery capability profile

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

Plan delivery