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AI-200: Develop AI cloud solutions on Azure
Data, cloud, and AI come together on Azure as a powerful platform. Anyone looking to build intelligent applications will find the tools and services they need right here.
Key topics
- Architecture and implementation of cloud-based AI applications on Azure.
- Using Azure AI Services and Azure OpenAI in practice.
- Building speech, image, and data pipelines with Azure services.
- Connecting AI models to existing applications via APIs.
- Designing AI workflows and automations with Azure.
- Planning secure and responsible AI systems.
Prerequisite
Those already familiar with programming, REST APIs, and basic Azure services can dive straight into the practical content.
Target group
Suited for anyone working with AI capabilities in a cloud environment – whether as a developer, cloud specialist, or in another technical role.
Azure technologies make it possible to tackle complex requirements with scalable, intelligent solutions.
Course Content
Running container applications on Azure
- Organize and manage container images using Azure Container Registry.
- Host and publish containers using Azure App Service.
Operating and scaling apps in Azure Container Apps
- Deploy apps to Azure Container Apps and get them running.
- Manage and adjust live containers as needed.
- Set up scaling rules for container-based applications.
Running and monitoring Kubernetes applications on Azure
- Deploy and operate applications using Azure Kubernetes Service.
- Configure and fine-tune environments within AKS.
- Monitor running applications and investigate issues.
Using Azure Cosmos DB for NoSQL in AI applications
- Develop NoSQL queries for Azure Cosmos DB.
- Implement vector-based search capabilities in Azure Cosmos DB for NoSQL.
- Optimize queries in Azure Cosmos DB for NoSQL for performance.
Building AI capabilities with Azure Database for PostgreSQL
- Create, manage, and query data in Azure Database for PostgreSQL.
- Set up vector search within Azure Database for PostgreSQL.
- Integrate AI features into PostgreSQL-based applications.
Using Azure Managed Redis to enhance AI solutions
- Implement data processing with Azure Managed Redis.
- Implement event-driven messaging processes using Azure Managed Redis.
- Use Azure Managed Redis as vector storage.
Connecting Azure backend services for AI workflows
- Organize and process AI tasks in queues using Azure Service Bus.
- Implement event-driven AI processes with Azure Event Grid.
- Develop serverless backend solutions for AI applications using Azure Functions.
Securing secrets and managing configurations for AI apps
- Protect secrets and credentials using Azure Key Vault.
- Manage application settings centrally with Azure App Configuration.
Monitoring and troubleshooting Azure applications
- Instrument apps with OpenTelemetry to collect telemetry data.
- Use logs and metrics to diagnose and resolve issues.
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
Why is Azure AI expertise becoming more and more valuable?
- AI is changing how applications are built. Being able to deploy Azure AI Services securely and at scale brings real value to cloud projects.
Which tools and platforms are covered in the AI-200 training?
- At the centre are Azure AI Foundry, Azure OpenAI Service, Azure AI Search, and other Azure AI Services – complemented by hands-on development environments.
What can you actually build with Azure AI in your organisation?
- Azure enables the implementation of a variety of AI applications—such as intelligent assistant systems, automated data analysis, and generative AI solutions for digital business processes.
What background knowledge should I have before starting?
- Hands-on experience in software development, familiarity with REST APIs, and a basic understanding of cloud environments are all helpful.
Where does generative AI come into play on Azure?
- Chatbots, intelligent search, content processing, and automated workflows are typical use cases for generative AI on Azure.
What sets professional cloud AI development apart?
- Responsible use, secure architecture, thoughtful data processing, and the right selection of Azure services form the foundation.
How does Azure AI know-how boost my career prospects?
- Skills in AI development, cloud architecture, and automation rank among the most sought-after IT competencies in the job market.
How does Azure speed up my AI projects in practice?
- Scalable platforms and ready-to-use services make it straightforward to bring AI models into production applications quickly and reliably.
