The AI‑200T00-A course focuses on the end-to-end development of AI-driven cloud applications using Microsoft Azure. It introduces learners to modern architectural patterns and technologies that support AI workloads, including containerization, serverless computing, event-driven systems, and cloud-native data services.
Rather than concentrating on how AI models are built, the course emphasizes how they are:
- Integrated into applications
- Connected with data sources
- Orchestrated across distributed systems
- Deployed and maintained in cloud environments
Participants gain practical experience in designing complete AI solutions, where multiple services work together seamlessly to deliver intelligent functionality.
Detailed Learning Experience and Modules
The course is structured into several focused learning paths, each addressing a critical component of modern AI cloud architecture. These modules collectively provide a comprehensive understanding of how to build robust AI applications from a backend and integration perspective.
1. Implementing Containerized Application Hosting
The course begins by introducing containerization, a core technology for modern cloud applications. Containers allow applications, including AI services, to be packaged and deployed consistently across environments.
Learners explore:
- How to build and store container images using Azure Container Registry
- Deploying applications using container-based services
- Managing application environments efficiently
This module establishes the foundation for building portable, scalable AI applications that can run reliably in any environment.
2. Deploying and Managing Applications with Azure Container Apps
Building on containerization, learners move into serverless container platforms such as Azure Container Apps.
In this module, participants learn:
- How to deploy applications without managing infrastructure
- How to configure scaling based on demand
- How to manage application versions and updates
This approach enables developers to create AI applications that automatically scale and adapt to workload changes, which is essential for handling real-world usage patterns.
3. Orchestrating Applications with Azure Kubernetes Service (AKS)
For more complex scenarios, the course introduces Azure Kubernetes Service (AKS), a powerful orchestration platform for managing containerized applications at scale.
Learners explore:
- Deploying applications in Kubernetes clusters
- Managing application configuration and secrets
- Monitoring and troubleshooting distributed systems
This module equips participants with the skills to design highly resilient and enterprise-grade AI systems capable of handling large-scale workloads.
4. Developing AI Solutions with Azure Data Services
AI applications depend heavily on data. This module focuses on how to design and use modern data services optimized for AI workloads.
Key technologies include:
- Azure Cosmos DB for NoSQL, including vector-based queries
- Azure Database for PostgreSQL (pgvector) for AI-driven data retrieval
- Azure Managed Redis for caching, streaming, and high-performance data access
Participants learn how to store, retrieve, and process data efficiently, enabling AI systems to deliver fast, accurate, and context-aware results.
5. Integrating Event-Driven and Messaging Architectures
Modern AI systems are often built as distributed, event-driven applications. This module introduces the tools and patterns used to connect services and enable real-time processing.
Learners work with:
- Azure Functions for serverless execution
- Azure Service Bus for reliable messaging
- Azure Event Grid for event-driven communication
This allows AI applications to respond dynamically to events, creating solutions that are responsive, scalable, and loosely coupled.
6. Connecting Services and Orchestrating AI Workflows
A key focus of the course is understanding how different components interact to form a complete system.
Participants learn:
- How to connect backend services with AI capabilities
- How to orchestrate workflows across multiple components
- How to design systems that integrate APIs, data, and compute resources
This module highlights the importance of system design and integration, turning isolated services into cohesive AI solutions.
7. Securing AI Applications
Security is a fundamental requirement for any production system. This module teaches learners how to protect AI applications and their data.
Topics include:
- Managing secrets with Azure Key Vault
- Implementing secure authentication and authorization
- Protecting data and services across distributed systems
This ensures that AI solutions are not only functional but also safe and compliant with enterprise standards.
8. Monitoring, Observability, and Troubleshooting
The final stage of the course focuses on ensuring that AI systems remain reliable and maintainable over time.
Learners explore:
- Monitoring application performance using Azure tools
- Collecting and analyzing logs and metrics
- Troubleshooting issues in distributed environments
This module introduces the concept of observability, enabling developers to understand how systems behave in production and continuously improve them.
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