Develop AI cloud solutions on Azure (AI-200T00)
(5 days)
Course Description
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.Learning Objectives
- Deploy and configure containerized microservices using Azure Container Apps and Azure Kubernetes Service
- Implement high-dimensional vector search capabilities using Azure Cosmos DB, Azure
- Database for PostgreSQL, and Azure Managed Redis
- Build decoupled, event-driven architectures utilizing Azure Service Bus, Event Grid, and serverless Azure Functions
- Secure application settings and credentials using Azure Key Vault and App Configuration
- Instrument applications with OpenTelemetry to collect distributed traces and metrics
- Analyze application performance and telemetry data using Application Insights and Azure Monitor
- Create proactive alerting dashboards to monitor the health and performance of distributed workloads
Who Should Attend
Developers who build backend and AI-driven applications on Azure and need practical skills in- Containerized compute
- Data services for AI
- Event-driven workflows
- Application security
- Monitoring and diagnostics
Prerequisites
- Programming experience with a language such as Python, JavaScript, or C#.
- A basic understanding of Azure services and cloud computing concepts.
- Familiarity with containerization concepts, including Docker and Kubernetes fundamentals.
- Familiarity with relational databases, JSON document structures, and SQL fundamentals.
- An understanding of AI and machine learning concepts like embeddings and vector similarity search.
