AI System Architecture on Azure
Certificate of Completion by Code.Hub
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.
By the end of this module, 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
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
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
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
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
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
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
Roles:
- AI Engineer
- Data Engineer
- Machine Learning Engineer
- AI Solutions Architect
Seniority:
- Junior
- Mid-Level
- Experience with cloud concepts and basic Azure services (compute, storage, networking)
- Familiarity with APIs, data processing, and basic AI/LLM concepts
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
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