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AI System Architecture on Azure

Description

This course provides a comprehensive approach to designing and implementing AI system architectures on Microsoft Azure. It covers how to build scalable, secure, and production-ready AI solutions using services such as Azure OpenAI, Azure Machine Learning, Cognitive Services, and data platforms. Participants will learn how to architect end-to-end pipelines that integrate data ingestion, model inference, RAG systems, and agent-based workflows. The course emphasizes cloud-native design principles, including microservices, event-driven architectures, and distributed systems. Hands-on labs focus on building real-world AI solutions with proper security, identity management, and monitoring. Special attention is given to governance, cost optimization, and performance tuning. By the end of the course, participants will be able to design enterprise-grade AI architectures on Azure.

 

🕒 Duration: 24 hours

👥 Target Audience:

  • Roles:
    AI Engineer, Data Engineer, Machine Learning Engineer, AI Solutions Architect

 

  • Seniority:
    Junior, Mid-Level
Webinar Content
Module 1: Foundations of Azure AI Architecture
Cloud & AI Fundamentals
 Introduction to AI Architecture
on Azure
  • Overview of Azure AI ecosystem
  • AI workloads and architecture patterns
  • Cloud-native AI principles

 

  • AI Practice: Use AI to design a high-level Azure AI architecture for a business case
Azure Core Services for AI
  • Compute (App Service, AKS)
  • Storage (Blob, Data Lake)
  • Networking basics

 

  • AI Practice: Generate Azure service selection based on requirements
Module 2: AI Services Integration
LLM & Cognitive Services
 Azure OpenAI Integration
  • LLM deployment on Azure
  • API usage and configuration
  • Prompt orchestration

 

  • AI Practice: Build a simple Azure OpenAI-powered API
Cognitive Services & AI APIs
  • Vision, speech, language services
  • Multi-modal AI integration

 

  • AI Practice: Combine multiple AI services in a workflow
Module 3: Data & RAG Systems
Knowledge Architectures
 Data Platforms for AI
  • Azure SQL, Cosmos DB, Data Lake
  • Data pipelines and ingestion

 

  • AI Practice: Design a data architecture for AI workloads
RAG on Azure
  • Azure AI Search / vector DB
  • Embeddings and retrieval
  • Context injection

 

  • AI Practice: Build a RAG pipeline using Azure services
Module 4: Microservices & APIs
Distributed Systems
 AI-Driven Microservices
  • ASP.NET Core APIs on Azure
  • Service communication patterns

 

  • AI Practice: Design microservices for an AI system
Event-Driven Architectures
  • Azure Service Bus / Event Grid
  • Streaming and async workflows

 

  • AI Practice: Generate event-driven flow using AI
Module 5: Security & Governance
Enterprise AI
 Identity & Access Management
  • Azure AD, RBAC
  • Secure API access

 

  • AI Practice: Design secure access strategy using AI
AI Governance & Compliance
  • Responsible AI principles
  • Data privacy and compliance

 

  • AI Practice: Evaluate risks and governance policies using AI
Module 6: Observability & Optimization
Production Systems
 Monitoring & Logging
  • Azure Monitor, Log Analytics
  • Observability for AI systems

 

  • AI Practice: Design monitoring strategy with AI assistance
Cost Optimization & Capstone
  • Cost control strategies
  • Scaling AI workloads
  • Final project

 

  • AI Practice: Build and optimize a full Azure AI architecture
Learning Objectives:

After attending this webinar participants will be able to:

  • Design end-to-end AI architectures using Azure services and cloud-native patterns
  • Integrate Azure OpenAI, data platforms, and APIs into scalable AI solutions
  • Implement RAG and agent-based systems within Azure environments
  • Apply security, identity, and governance best practices for AI workloads
  • Optimize performance, cost, and observability in production AI systems
Prerequisite Knowledge
  • Experience with cloud concepts and basic Azure services (compute, storage, networking)
  • Familiarity with APIs, data processing, and basic AI/LLM concepts

 

Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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