Module 1 – Modern AI Application Architecture
This module introduces the building blocks of intelligent cloud applications.
Topics
- Evolution of AI-powered software
- Azure AI ecosystem
- Large Language Models
- Cloud-native AI architecture
- Selecting AI services
- Designing scalable AI solutions
Practical Lab
- Creating a foundational AI application
Module 2 – Building Generative AI Solutions
Participants learn how to create applications powered by modern language models.
Topics
- Working with foundation models
- Prompt design strategies
- Context management
- Response generation
- Output optimisation
- AI workflow design
Practical Lab
- Developing a document assistant
- Improving prompt quality
Module 3 – Developing Intelligent AI Agents
This module focuses on creating AI agents capable of planning and executing complex tasks.
Topics
- Agent design principles
- Planning and reasoning
- Memory management
- Tool invocation
- Multi-step execution
- Agent orchestration
Practical Lab
- Building an enterprise AI assistant
- Implementing autonomous workflows
Module 4 – Enterprise Knowledge Integration
Learn how to connect AI applications with business information and external systems.
Topics
- Retrieval-Augmented Generation
- Enterprise knowledge repositories
- Vector search concepts
- API integration
- Context enrichment
- Knowledge retrieval optimisation
Practical Lab
- Building an AI knowledge assistant
- Connecting enterprise data sources
Module 5 – Conversational and Multimodal AI
Participants develop intelligent applications capable of understanding multiple forms of input.
Topics
- Conversational AI design
- Natural language understanding
- Text generation
- Image analysis
- Multimodal reasoning
- Document intelligence
Practical Lab
- Creating a multimodal chatbot
- Analysing documents and images
Module 6 – Deploying AI Solutions on Azure
This module focuses on operationalising AI applications within cloud environments.
Topics
- Azure deployment strategies
- Model serving
- API management
- Performance optimisation
- Scaling AI workloads
- Monitoring and logging
Practical Lab
- Deploying an AI application to Azure
- Configuring operational monitoring
Module 7 – Secure and Responsible AI Development
Participants explore governance, security, and operational best practices for enterprise AI.
Topics
- Responsible AI principles
- Security and identity management
- Data protection
- Compliance considerations
- Model evaluation
- Continuous improvement
Practical Lab
- Implementing governance controls
- Evaluating AI application quality
Practical Learning
Throughout the course participants will:
- Build Generative AI applications.
- Develop intelligent AI agents.
- Integrate enterprise knowledge sources.
- Create Retrieval-Augmented Generation solutions.
- Implement multimodal AI workflows.
- Deploy cloud-based AI services.
- Monitor application performance.
- Apply responsible AI and security practices.
- Develop production-ready Azure AI solutions.
Skills Gained
Upon completion of this course, participants will be able to design, develop, deploy, and maintain intelligent AI applications on Microsoft Azure. They will understand how to combine large language models, AI agents, enterprise knowledge retrieval, multimodal capabilities, and cloud-native services to deliver secure, scalable, and production-ready AI solutions that address real-world business challenges.
