The course is structured into a series of comprehensive modules, organized across multiple learning paths that reflect how AI systems are built in practice. Each module builds on the previous one, gradually moving from foundational setup to advanced implementation and orchestration.
1. Planning and Preparing AI Solutions on Azure
The course begins by establishing the technical and architectural foundation required for AI development. Learners are introduced to Azure AI Foundry as a unified platform for managing models, tools, and workflows.
Key areas of focus include:
- Understanding the AI development lifecycle
- Exploring Azure AI Foundry tools and SDKs
- Setting up development environments
- Applying responsible AI principles from the start
This module prepares learners to approach AI development with the right structure, tools, and governance practices.
2. Selecting, Deploying, and Evaluating AI Models
Once the foundation is in place, learners explore how to choose and deploy AI models effectively. This includes working with model catalogs, comparing performance benchmarks, and deploying models to scalable endpoints.
Participants learn:
- How to select appropriate models for specific use cases
- Methods for deploying models in production environments
- Techniques for evaluating model accuracy and performance
This module is critical for understanding how AI systems are designed with performance, scalability, and reliability in mind.
3. Developing Generative AI Applications
A central part of the course focuses on building generative AI applications, which are at the core of modern AI innovation.
Learners work with:
- Large Language Models (LLMs)
- Chat-based applications and conversational systems
- APIs for generating responses and interacting with users
Through practical examples, participants learn how to create applications that can generate content, answer questions, and assist users intelligently.
4. Extending AI with Tools and Integrations
Modern AI systems rarely operate in isolation—they interact with external systems, data sources, and services. This module explores how to extend AI applications using tools and integrations.
Topics include:
- Connecting AI models to external APIs
- Enabling models to perform real-world tasks
- Using tools to enhance application functionality
This allows AI systems to move beyond simple conversations and become action-oriented solutions capable of solving real business problems.
5. Optimizing AI Performance
As AI solutions grow in complexity, optimization becomes essential. This module teaches learners how to improve the quality, efficiency, and reliability of AI applications.
Key techniques include:
- Prompt engineering to guide model behavior
- Retrieval-Augmented Generation (RAG) for grounding responses in data
- Fine-tuning models for consistent results
Learners gain practical insight into how to refine AI outputs and ensure high-quality performance in production environments.
6. Implementing Responsible AI Practices
With powerful AI capabilities comes the responsibility to use them ethically. This module focuses on ensuring that AI systems are safe, fair, and compliant.
Learners explore:
- Identifying and mitigating potential risks
- Applying content filtering and safety measures
- Designing AI solutions that align with ethical standards
Responsible AI is not treated as an afterthought, but as a core component of professional AI development.
7. Building and Managing AI Agents
One of the most advanced and exciting areas of the course is the development of AI agents—systems that can perform tasks autonomously, make decisions, and interact with tools.
Participants learn:
- What AI agents are and how they function
- How to design and build agent-based systems
- How to manage agent workflows and behavior
This introduces learners to the concept of agentic AI, which represents the next generation of intelligent applications.
8. Integrating Custom Tools and Knowledge Systems
To build truly powerful AI solutions, agents must be able to access external knowledge and tools.
In this module, learners explore:
- Integrating custom APIs and enterprise systems
- Building knowledge-enhanced applications using data sources
- Applying Retrieval-Augmented Generation (RAG) at scale
This enables AI systems to become more accurate, context-aware, and useful in real-world scenarios.
9. Building Advanced Agent Workflows and Multi-Agent Systems
The course concludes with advanced topics such as:
- Designing multi-agent architectures
- Orchestrating complex workflows across multiple agents
- Integrating AI solutions with platforms such as Microsoft 365
These capabilities allow developers to create sophisticated AI ecosystems that automate tasks, enhance productivity, and deliver real business value.
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