Build AI Solutions with Microsoft Foundry
Build and deploy AI solutions with Microsoft Foundry — models, prompts, and agent orchestration.
About This Course
Microsoft Foundry is the platform where you build, deploy, and manage AI solutions on Microsoft's infrastructure. This course covers the full development lifecycle — from model selection and deployment, through prompt engineering and flow orchestration, to agent building and production monitoring.
You will deploy models from the model catalog — GPT-4o, Phi, Llama, and other open-source models — and configure them for your use cases. The prompt flow section teaches you to build orchestration pipelines that chain multiple AI calls together, integrate with external data sources, and handle the error cases that real applications encounter. Agent development is a major focus: you will build agents that use tool calling, maintain conversation state, and execute multi-step reasoning workflows.
Production readiness separates a demo from a real deployment. The course covers content safety filters, evaluation frameworks for measuring model performance, and monitoring dashboards that track latency, token usage, and failure rates. If you are building AI applications on Microsoft's platform, this course teaches you to do it properly.
What You'll Learn
Deploy and configure AI models in Microsoft Foundry including GPT, Phi, and open-source models
Design prompt flows and orchestration pipelines for production AI applications
Build and deploy AI agents with tool calling and multi-step reasoning
Implement content safety, evaluation, and monitoring for deployed AI solutions
Prerequisites
- Familiarity with AI concepts and basic programming is helpful
- An Azure subscription to access Microsoft Foundry — a free trial works
Who Is This Course For
This course is for developers, AI engineers, and solution architects who are building AI applications on Microsoft Foundry. If your organization is developing custom AI solutions — chatbots, document processing, data analysis, or agent-based workflows — this course gives you the practical skills to build and deploy them. Familiarity with AI concepts and basic programming is helpful.
What's Inside
Model Deployment and Configuration
- Model catalog and selection criteria
- GPT, Phi, and open-source model deployment
- Model configuration and parameter tuning
- Managed endpoints and serverless deployment
Prompt Engineering and Flows
- Prompt design patterns and techniques
- Prompt flow creation and orchestration
- Data integration and grounding
- Testing and iterating on prompt flows
Agent Development
- Agent architecture and capabilities
- Tool calling and function integration
- Multi-step reasoning and planning
- Conversation management and state handling
Production Operations
- Content safety filters and responsible AI
- Evaluation frameworks and benchmarking
- Monitoring and observability for AI deployments
- Cost management and optimization