Develop AI Cloud Solutions on Microsoft Azure (AI-200)
Certification Preparation for the "Azure AI Developer Associate"
.svg)
Machine Learning Engineer
1. Implementing Container Application Hosting on Azure
Learn about the key workflows for container hosting on Azure, including image management with Azure Container Registry and deploying custom containers to Azure App Service with runtime configuration.
2. Deploying and Managing Apps in Azure Container Apps
This module covers the entire lifecycle of containerized applications in Azure Container Apps, including deployment, configuration, version management, and setting up automatic horizontal scaling.
3. Deploying and Monitoring Applications on Azure Kubernetes Service
A guide to the entire AKS lifecycle, covering deployment with manifests and services, externalizing configuration with ConfigMaps and Secrets, connecting to persistent storage, and monitoring application health.
4. Developing AI Solutions with Azure Cosmos DB for NoSQL
The focus is on developing AI solutions with Azure Cosmos DB for NoSQL by building a data foundation, implementing vector search functions, and optimizing query performance.
5. Developing AI Solutions with Azure Database for PostgreSQL
This module guides you through the process of developing AI solutions with Azure Database for PostgreSQL by building a data foundation, implementing vector search using the pgvector extension, and optimizing performance.
6. Optimize AI Solutions with Azure Managed Redis
Learn how you can use Azure Managed Redis to optimize your AI solutions, including caching strategies, data operations, event streaming, and vector storage.
7. Integrate Backend Services for AI Solutions
Integrate backend services such as Azure Service Bus, Azure Event Grid, and Azure Functions to build reliable, event-driven, serverless AI solutions on Azure.
8. Managing Application Secrets and Configurations for AI Solutions
Learn how to securely manage secrets using Azure Key Vault and centralize application configuration—including feature flags—with Azure App Configuration.
9. Monitoring and Troubleshooting Apps on Azure
In the final module, you'll learn how to achieve end-to-end observability for distributed AI applications on Azure by instrumenting them with OpenTelemetry, exporting telemetry data to Application Insights, and analyzing data using KQL queries and alerts.
Requirements:
- Programming experience with languages such as Python, JavaScript, or C#
- Basic understanding of Azure services and cloud computing concepts
- Knowledge of the basics of containerization
- Explain the container lifecycle using Azure Container Registry (ACR), App Service, and Container Apps
- Secure configurations using Kubernetes primitives (ConfigMaps, Secrets) and Azure Key Vault
- Implementing Vector Search in Azure Cosmos DB, PostgreSQL (with pgvector), and Managed Redis
- Optimize container scaling and resource allocation using scaling rules and KEDA scalers
- Decouple AI components using Azure Service Bus (queues/topics) and reliable task queues with Redis Streams
- Build Event-Driven Architectures with Azure Event Grid and the CloudEvents Schema
- Troubleshoot distributed AI workloads by configuring OpenTelemetry for Azure Monitor Application Insights and using KQL analysis
- Securely connect applications using Microsoft Entra authentication and the secret management features of Key Vault/App Configuration
This course consists of antraining is led by an instructor who provides live guidance to participants. Theory and practice are taught through live demonstrations and hands-on exercises. The course uses the video conferencing software Zoom.
This course is designed for developers who build backend and AI-powered applications on Azure and need hands-on knowledge of containerized computing environments, data services for AI, event-driven workflows, and application security and monitoring.
.svg)
