AI-200: Develop AI Cloud Solutions on Azure

The AI-200T00: Develop AI Cloud Solutions on Azure course is designed for developers who want to build, deploy, and manage modern AI-powered applications using Microsoft Azure. Participants will learn how to create scalable cloud solutions, connect AI services, manage data effectively, and implement secure and reliable architectures for real-world business scenarios. Throughout the course, students gain practical experience with Azure services used to support intelligent applications, including containerized workloads, serverless computing, databases, event-driven integrations, monitoring, and security. 

By the end of the training, participants will have the skills needed to develop robust AI solutions that can be deployed, monitored, and maintained in enterprise environments.

1. Implement Container Application Hosting on Azure

Learn how modern applications are packaged and deployed using containers. Participants will discover how containers simplify application deployment and ensure consistency across development, testing, and production environments. The course introduces Azure Container Registry and best practices for managing container images and deployments.

2. Deploy and Manage Applications with Azure Container Apps

Explore how to deploy cloud-native applications without managing complex infrastructure. Students will learn how Azure Container Apps can automatically scale applications based on demand, making it easier to build efficient and cost-effective AI services. This module focuses on simplifying application management while maintaining high availability and performance.

3. Deploy and Monitor Applications on Azure Kubernetes Service (AKS)

Understand how large-scale applications are managed using Kubernetes. Participants learn how AKS helps organizations run containerized workloads, automate deployments, improve reliability, and manage application lifecycles. The training also covers monitoring tools that help teams maintain healthy and responsive AI applications.

4. Develop AI Solutions with Azure Cosmos DB for NoSQL

Learn how to store and manage large volumes of data for AI applications. Students will explore Azure Cosmos DB and understand how it supports high-performance applications that require fast access to structured and unstructured information. Special attention is given to designing databases that support modern AI workloads and intelligent applications.

5. Develop AI Solutions with Azure Database for PostgreSQL

Discover how relational databases can support AI-driven solutions. Participants learn to work with Azure Database for PostgreSQL, including capabilities that help developers store, process, and retrieve data efficiently for AI applications and machine learning scenarios.

6. Enhance AI Solutions with Azure Managed Redis

Learn how caching and high-speed data access improve application performance. This module explains how Azure Managed Redis helps reduce response times, improve user experience, and support modern AI scenarios such as vector search and real-time application processing.

7. Integrate Backend Services for AI Solutions

Explore how different services communicate within an AI ecosystem. Participants will work with event-driven and message-based architectures using Azure Service Bus and Event Grid. This allows applications to exchange information reliably, process events efficiently, and automate workflows across multiple systems.

8. Manage Application Secrets and Configuration

Security is a critical component of every AI solution. In this module, students learn how to securely manage passwords, API keys, connection strings, and application settings using Azure Key Vault and configuration management services. The focus is on protecting sensitive information while simplifying application administration.

9. Observe and Troubleshoot Applications on Azure

Learn how to monitor application health, detect issues, and improve performance. Participants will use Azure monitoring and observability tools to track application activity, analyze logs, identify bottlenecks, and ensure that AI solutions remain reliable in production environments.

This course is intended for:

• Software Developers and Application Developers
• Cloud Developers working with Microsoft Azure
• AI Engineers who want to strengthen their cloud development skills
• Backend Developers building intelligent applications
• Technical Professionals involved in AI solution implementation
• Anyone looking to prepare for Azure AI Cloud Developer certification

Participants should have basic knowledge of application development and familiarity with Azure fundamentals.

Upon successful completion of the training, participants will receive an official Microsoft Certificate of Completion, validating their attendance and acquired skills in developing AI cloud solutions on Microsoft Azure.

In addition, learners can earn a Microsoft Learn Achievement Badge, a digital credential that showcases their newly gained knowledge and skills. This badge can be shared on professional platforms such as LinkedIn, included in digital portfolios, and used to demonstrate expertise in Azure AI and cloud development technologies. The course is also aligned with the skills measured for the Microsoft Certified: Azure AI Cloud Developer Associate certification, helping participants prepare for certification-related objectives and career advancement opportunities.